A network pharmacology analysis on drug-like compounds from Ganoderma lucidum for alleviation of Atherosclerosis

Background: Ganoderma lucidum (GL) is known as a potent alleviator against chronic inammatory disease like atherosclerosis (AS), but its critical bioactive compounds and their mechanisms against AS have not been unveiled. This research aimed to identify the key compounds(s) and mechanism(s) of GL against AS through network pharmacology. Methods: The compounds from GL were identied by gas chromatography-mass spectrum (GC-MS), and SwissADME screened their physicochemical properties. Then, the gene(s) associated with the screened compound(s) or AS related genes were identied by public databases, and we selected the overlapping genes using a Venn diagram. The networks between overlapping genes and compounds were visualized, constructed, and analyzed by RStudio. Finally, we performed molecular docking test (MDT) to identify key gene(s), compound(s) on AutoDockVina. Results: A total of 35 compounds in GL was detected via GC-MS, and 34 compounds (accepted by the Lipinski's rule) were selected as drug-like compounds (DLCs). A total of 34 compounds were connected to the number of 785 genes and 2,606 AS-related genes were identied by DisGeNET and Online Mendelian Inheritance in Man (OMIM). The nal 98 overlapping genes were extracted between the compounds-genes network and AS-related genes. On Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, the number of 27 signaling pathways were sorted out, and a hub signaling pathway (MAPK signaling pathway), a core gene (PRKCA), and a key compound (Benzamide, 4-acetyl-N-(2,6-dimethylphenyl)) were selected among the 27 signaling pathways via MDT. Conclusion: Overall, we found that the identied 3 DLCs from GL have potent anti-inammatory ecacy, improving AS by inactivating the MAPK signaling pathway.

The natural product extracts are relatively safe, although some include unexpected adverse side effects such as headache, vomiting, dizziness, and constipation [8]. Also, medicinal herbal plants have been considered one of the most substantial therapeutics for alleviating and preventing congestive heart failure, including AS [9]. The medicinal mushrooms categorized into natural products also have diverse pharmacological effects, including antimicrobial, antitumor, anti-in ammatory, and antiatherogenic activities [10]. Among medicinal mushrooms, Ganoderma lucidum (GL) is used as a bene cial antiin ammatory agent and cardiovascular diseases therapy [11,12]. Korean Herbal Pharmacopoeia noted that GL could be used to treat AS, where the dosage of GL can be taken to maximum 18g/day. An animal test reported that GL has anti-atherogenic effect, demonstrated by reducing oxidative stress and in ammation [13]. In addition, the human monocytic cell line (THP-1) macrophage experiment indicated that GL diminished inducible nitric oxide synthase (iNOS) expression level, which led to alleviation of AS [14]. Besides, most studies reported that β-glucan polysaccharide isolated from GL revealed as a potent antioxidant against AS [15][16][17]. However, a clinical study for β-glucan polysaccharide e cacy on innate immune responses in humans (10 healthy male volunteers) showed that β-glucan polysaccharide was not detected in serum samples after receiving of 1000 mg once daily for 7 days [18], which indicated low pharmacokinetic properties. Generally, ligands' cell permeability is reduced typically when the topological polar surface area (TPSA) exceeds 140 Å 2 [19]. As a result, we infer that the β-glucan is very soluble, and cannot be accepted as a potent molecule due to inappropriate drug-like properties (TPSA: 268.68 Å 2 or Lipinski's rule violation). Other drug-like compounds (DLCs) and mechanism(s) of GL against AS have not been established completely, which should be substantiated to treat AS effectively.
Furthermore, natural product extracts have synergistic effects through the combination of some active compounds [20].
Hence, we performed compound-gene and gene-gene networks analysis of GL ethanolic extract via network pharmacology concept. Network pharmacology is a systematic analysis method to investigate synergistic e cacy and potential mechanism(s) of multiple molecules on system biology-based methodology [21,22]. This analysis provides a point of regulation between multi-signaling pathways and multi-molecule-gene-pathway regulation, hinting at new drug development for clinical trials [23]. It is an e cient method to understand the mechanism of actions on herbal medicines and decipher multiplemolecules in the bioprocess network [24]. Currently, network pharmacology is used to investigate new target genes and compounds' relationships, which is optimal to discover the therapeutic potentiality of natural products [25].
In this study, network pharmacology was utilized to investigate the network of compounds-target proteins-atherosclerosis from GL with a holistic perspective. In particular, we selected only DLCs through both GC-MS analysis and physicochemical properties prediction in in-silico. Then, target genes related to DLCs or AS were selected by public databases and identi ed overlapping genes between DLCs and AS were extracted. Thereby, compound(s)-gene(s)-signaling pathway(s) of GL on AS were identi ed by analyzing their interactions. Finally, key compound(s), key gene(s), and a hub signaling pathway of GL against AS were revealed via molecular docking test (MDT). The work ow diagram is exhibited in Figure   1.

Plant preparation, extraction
The dried and powdered GL (50 g) was soaked in 500 mL of 100% ethanol (Daejung, Korea). The extraction was carried out in a sealed bottle for 7 days and repeated 3 times at room temperature (20~22℃). During extraction processing, the sample was shaken several times to increase the yield rate. The ethanol was evaporated using a vacuum evaporator (IKA-RV8, Japan). The evaporated sample was dried under a digital heating bath (IKA-HB10, Japan) at 40 °C.

GC-MS condition
The analysis was carried out using GC-MS system (Agilent 7890A, 5975C Agilent Technologies Inc., Santa Rosa, CA, USA), is equipped with a DB-5 capillary column (30m×0.25mm×0.25μm). Firstly, the GC-MS instrument was maintained at a temperature of 100 °C for 2.1 minutes. The temperature rose to 300°C at the rate of 25°C/min and was maintained for 10 minutes at the end of this period. Injection port temperature and helium ow rate were maintained as 250 °C and 1.5 ml/min. The samples injected in split mode at 10:1 and the ionization voltage were 70 eV. MS scan range was set at 35-900 (m/z) and the fragmentation patterns of mass spectra compared in W8N05ST Library MS database. The relative peak area of each compound in the chromatogram was calculated on each compound percentage.
ChemStation integrater algorithms used as the concept of integration [26].

The detection of GL chemical compounds by GC-MS and identi cation of DLCs
The chemical compounds detected from GL via GC-MS input into the PubChem (https://pubchem.ncbi.nlm.nih.gov/) to select Simpli ed Molecular Input Line Entry System (SMILES) format. The screen of "Drug-like" property is based on Lipinski's rule in SwissADME (http://www.swissadme.ch/).
2.5. Signaling pathways of AS -targeted overlapping genes from DLCs-targeted genes Based on rich factor, signaling pathways of AS-targeted overlapping genes were analyzed by RStudio. The targets genes associated with signaling pathways were identi ed by STRING and constructed a size map. Degree value of signaling pathways -target genes represents the edges numbers of signaling pathways on target genes in network. The greater degree of genes is the more essential genes for GL's mechanism of action on AS.
2.6. Identi cation of a hub signaling pathway and key genes against AS There were 27 signaling pathways related to the occurrence and development of AS. Identi cation of a hub signaling pathway based on rich factor and key genes connected to a hub signaling pathway identi ed by STRING.

Preparation for MDT of potential key compounds
The potential key compounds were rst converted into .sdf from PubChem followed by .pdb format using Pymol, and later, the potential key compounds were converted into .pdbqt format through Autodock [26].

2.9
Potential key compounds -target protein docking simulation setting The ligand molecules were docked with target proteins utilizing autodock4 by setting-up 4 energy range and 8 exhaustiveness as default to obtain 10 different poses of ligand molecules [26]. The 2D binding interactions were identi ed through LigPlot+ v.2.2 (https://www.ebi.ac.uk/thorntonsrv/software/LigPlus/). After docking, a key compound of lowest binding energy was selected to visualize the ligand-protein interaction in Pymol.

Overlapping genes between 189 genes (SEA and STP overlapping genes) and AS-related genes
A total of 2,606 genes associated with AS were selected by using DisGeNET and OMIM human gene databases (Supplementary Table S2). The Venn diagram shows that 98 overlapping genes was identi ed between 2,606 genes related to AS and the 189 overlapping genes ( Figure 4 Table   S3). The gene-gene network of the 189 overlapping genes was constructed by STRING, which indicated 98 nodes and 134 edges (Figure 4-B).

Identi cation of a hub signaling pathway on a bubble plot
The KEGG analysis indicated that a total of overlapping 98 genes was enriched 27 signaling pathways (pvalue < 0.05) ( Figure 5) (Table 3). This analysis is for identifying the AS-related signaling pathways from the number of 98 genes. A hub signaling pathway (inactivation of MAPK signaling pathway) and connected to 7 key genes (HSPB1, PDGFRB, PRKCB, PRKCA, MAPK14, RELA, and PLA2G4A) against AS was identi ed on STRING analysis.

Acquisition of core genes related to signaling pathways
The number of 34 core genes obtained by KEGG pathway enrichment analysis and PRKCA manifested the highest degree (16), which was followed by PRKCB (15), MAPK14 (14), and RELA (14) (Figure 6), (Table 4). Accordingly, PRKCA was the uppermost gene of GL against AS.

MDT of 7 genes and 15 compounds related to MAPK signaling pathway
The number of 7 genes and 15 compounds associated with MAPK signaling pathway was identi ed by both SEA and STP databases (Figure-7A). Also, each 6 genes excluding PDGFRB gene was strongly related to MAPK signaling pathway (Figure-7b). The MDT was performed to evaluate the binding a nity energy of these 15 compounds against their related each gene, respectively. The MDT of A1-A2 on HSPB1 protein (PDB ID: 4MJH) was analyzed in the "Homo Sapiens" setting. According to the docking score, the priority of binding energy is given: A1>A2. The two binding energy of A1-HSPB1 and A2-HSPB1 revealed -8.8 and -6.6 kcal/mol, respectively. The Benzamide, 4-acetyl-N-(2,6-dimethylphenyl)-(PubChem ID: 147129) (A1) had the strongest a nity on HSPB1. The MDT between B1-B2 and PDGFRB protein (PDB ID: 3MJG) in the "Homo sapiens" setting shown binding a nity of each ligand, and the order of the priority of binding energy is as follows: B1>B2. The two-binding energy of B1-PDGFRB and B2-PDGFRB revealed -8.2 and -7.9 kcal/mol, respectively. The 3-[4-Methoxyphenyl] quinolin-4-ol (PubChem ID: 279953) (B1) had the strongest a nity on PDGFRB. The MDT between C1-C3 and PRKCB protein (PDB ID: 2I0E) in the "Homo sapiens" setting exposed binding a nity of each ligand, and the order of the priority of binding energy is as follows: C1>C3>C2. The two-binding energy of C1-PRKCB, C2-PRKCB and C3-PRKCB exhibited -8.4, -6.2, and -7.2 kcal/mol, respectively. The 1, 2, 3, 4-Tetrahydro-9-methyl-6cyclohexyl-1-carbazolone (PubChem ID: 535444) (C1) had the strongest a nity on PRKCB. The MDT between D1-D11 and PRKCA protein (PDB ID: 3IW4) in the "Homo sapiens" setting shown binding a nity of each ligand, and the order of the priority of binding energy is as follows: The MDT between F1-F2 and RELA protein (PDB ID: 2O61) in the "Homo sapiens" setting shown binding a nity of each ligand, and the order of the priority of binding energy is as follows: F1>F2. The two-binding energy of F1-RELA and F2-RELA revealed -8.2 and -4.0 kcal/mol, respectively. Interestingly, the MDT of 5 compounds (G1-G5) on PLA2G4A has inactive a nity scores (> -6.0 kcal*mol -1 ), thereby we did not consider them as promising molecule candidates against AS. The threshold of AutoDock Vina program is regarded as active molecules (binding a nity value < -6.0 kcal*mol -1 ) and inactive molecules (binding a nity value > -6.0 kcal*mol -1 ) [28]. The information is enlisted in Table 5.

Comparative MDT against positive controls on target proteins
The comparative MDT was performed to evaluate a nity between the highest docking score ligands and positive controls. Each positive control of target protein is as follows. The MDT of HSP27 inhibitor J2  Table 6.

Discussion
DLCs-genes network indicated that the therapeutic e cacy of GL on AS was related to 98 genes. The KEGG pathway enrichment analysis indicated that the number of 34 genes among 98 genes was connected to 27 signaling pathways related to the progression of AS, and might be implicated as the mechanism of GL against AS. Thus, interrelations of the 27 signaling pathways are concisely discussed as follows. Prolactin signaling pathway: Prolactin modulated the in ammatory response level in atherosclerotic lesions and showed a high expression level of prolactin receptors in atherosclerotic patients [29]. It implies that prolactin might be highly expressed in AS. PPAR signaling pathway: PPARα, PPARγ, and PPARβ/δ inhibited the atherogenic in ammation and interrupted the accumulation of cholesterol [30]. VEGF signaling pathway: Expression level of VEGF in AS was increased during the development of disease [31]. Fc epsilon RI signaling pathway: Fc epsilon stimulated macrophage cell and consecutively formed foam cell to aggravate AS lesions [32]. Adipocytokine signaling pathway: Adipocytokine contributes to endothelial dysfunction, vascular in ammation, and plaque formation, all of which are lesions of AS [33]. AGE-RAGE signaling pathway in diabetic complications: AGE-RAGE in diabetes aggravated AS, and RAGE might be a therapeutic target in in ammatory responses in vascular disease [34]. Thyroid hormone signaling pathway: Both hyperthyroidism and hypothyroidism induce cardiovascular disease; moreover, hypothyroidism is related to AS [35]. Sphingolipid signaling pathway: The atherosclerotic lesions comprise many sphingolipids, and sphingolipid synthesis inhibition is a potential mechanism of action against AS [36]. GnRH signaling pathway: GnRH receptor was highly expressed in atherosclerotic plaque, associated with cytokine activation [37,38]. IL-17 signaling pathway: IL-17 expression level increased in early atherosclerotic lesions, conversely, decreased in the advanced stage of AS [39]. B cell receptor signaling pathway: B cell components were involved in atherosclerotic development via antibody production, which led to the direct regulation of immune cells [40]. T cell receptor signaling pathway: Activated T cells translocated into atherosclerotic plaques and its activation was regarded as a marker to be indicated the atherosclerotic plaques [41,42]. HIF-1 signaling pathway: HIF-1 had a signi cant role in developing AS by producing foam cells with cholesterol [43]. Calcium signaling pathway: Calcium signaling pathway in uences cytokine production in vascular disease [44]. FoxO signaling pathway: FoxO deacetylation could aggravate the atherosclerotic plaque formation and lead to the in ammatory responses in vascular disease [45]. ErbB signaling pathway: ErbB signaling activation progresses in ammation in vascular disease, and its inhibition might be an important target to alleviate the atherosclerotic lesions [46]. Relaxin signaling pathway: Relaxin is a hormone that has an inhibitory agent to relieve the atherosclerotic-associated lesions [47]. Wnt signaling pathway: Activation of Wnt signaling pathway interrupts lipid accumulation, foam cell production, and further prevents AS [48]. Chemokine signaling pathway: Chemokines interrupt smooth muscle cell, and platelet activation to induce atherosclerotic processing [49]. Phospholipase D signaling pathway: Knock-out mice of Phospholipase D enzyme consumed more food than control animals, which led to fatty acid overaccumulation in the body [50]. It implies that Phospholipase D enzyme facilitates the metabolic processing against AS. TNF signaling pathway: TNF p55 receptors blocked atherosclerotic lesion progression in the mouse [51]. It implicates that activation of TNF p55 receptors might inhibit AS development. Oxytocin signaling pathway: An animal study suggested that oxytocin treatment diminished the level of IL-6 over 6 hours and alleviated AS lesions in the thoracic aorta [52]. Ras signaling pathway: Ras is a signaling molecule involved in AS development and vascular in ammation [53]. Neutrophin signaling pathway: The neutrophin signaling pathway was connected to AS-related ocular complications [54]. AMPK signaling pathway: The activation of AMPK signaling pathway plays a vital role in development of AS and its inhibition might be a potential target against AS [55]. Rap1 signaling pathway: Rap1 has anti-in ammatory signaling to block the development of AS, conversely, the activation of Rap1 increases atherosclerotic plaque [56]. MAPK signaling pathway: The MAPK is involved directly in proin ammatory responses, oxidative stress, and cytokines abundant in AS lesions [57]. It supports that inhibition of MAPK might be to ameliorate atherosclerotic lesions. The number of 3 DLCs related to 6 (excluding PLA2G4A) out of 7 target genes of MAPK signaling pathway demonstrated higher a nity score than positive controls. Among 7 target genes, PRKCA gene plays signi cant roles in 16 signaling pathways out of 27 signaling pathways by MAPK signaling pathway. The Benzamide, 4-acetyl-N-(2, 6-dimethylphenyl)-(PubChem ID: 147129) showed the highest binding a nity with 3 genes (HSPB1, PRKCA, and MAPK14) out of 7 target genes on MAPK signaling pathway, suggesting multi-target synergistic e cacy against AS.

Conclusion
Our ndings via network pharmacology analysis indicated that the number of 34 genes was de ned as "core genes" and PRKCA gene had the highest degree value that interacted 16 out of 27 signaling pathways. Through MDT, Benzamide, 4-acetyl-N-(2,6-dimethylphenyl) (PubChem ID: 147129); manifested the highest a nity score on PRKCA gene. Furthermore, both 3-[4-Methoxyphenyl] quinolin-4-ol (PubChem ID: 279953) and 1,2,3,4-Tetrahydro-9-methyl-6-cyclohexyl-1-carbazolone (PubChem ID: 535444) exposed higher a nity score on target proteins than positive controls. We elucidate that signi cant druggable 3 compounds in GL might be used for the treatment of AS. Among the 3 druggable compounds, MDT revealed that Benzamide, 4-acetyl-N-(2,6-dimethylphenyl)-had the most substantial inhibitory effect on PRKCA gene in MAPK signaling pathway. To sum things up, based on the holistic viewpoints, we concluded that Benzamide, 4-acetyl-N-(2,6-dimethylphenyl) (PubChem ID: 147129) in GL is the most potent bioactive to alleviate in ammatory responses against AS by inactivating PRKCA gene (a key gene) followed by inhibiting MAPK signaling pathway (a hub signaling pathway) on KEGG pathway. The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any quali ed researcher. The datasets generated and/or analysed during the current study are available in DisGeNET (https://www.disgenet.org/search) and OMIM (https://www.ncbi.nlm.nih.gov/omim).

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Competing interests
The authors declare that they have no competing interests.

Funding
This research did not receive any speci c grant from funding agencies in the public, commercial, or notfor-pro t sectors.
Author Contributions K.K.O. conducted the conceptualization, methodology, formal analysis, data curation, original draft writing. M.A. interpreted the results, reviewed, and edited the original draft. D.H.C. investigated, reviewed, and interpreted the results. All authors read, critically reviewed, and consented to the manuscript.     Table 6. Comparative MDT between the best docking ligands in GL and positive controls.