Skip to main content
NCBI home page
3 Biotech logoLink to 3 Biotech
. 2022 Oct 6;12(11):312. doi: 10.1007/s13205-022-03362-5

Network pharmacology-based analysis of signaling pathways of an anti-osteoporotic triterpenoid from Acyranthes bidentata Blume root

Ki Kwang Oh 1,✉
PMCID: PMC9537396  PMID: 36276446

Abstract

In Korea folk remedies, Acyranthes bidentata Blume is a functional food plant to treat bone diseases; especially, its roots have been used to alleviate osteoporosis (OP), but its key chemical compound(s) and mechanism of action against osteoporosis have not reported yet. This study suggests that Acyranthes bidentata Blume root (ABBR) has promising compound(s) against OP. We utilized network pharmacology to evaluate the therapeutic value. The chemical compounds from Acyranthes bidentata Blume root (ABBR) were identified by gas chromatography-mass spectrum (GC–MS); their physicochemical properties have been evaluated by SwissADME. Next, the target(s) related to a triterpenoid or OP-related targets were investigated by public databases. The signaling pathways from final targets were visualized, constructed, and analyzed by RPackage. Finally, we performed a molecular docking (MD) to explore key target(s) and compound(s) by employing AutoDockVina tools; the residues of amino acids interacted with ligands were identified by LigPlot + v.22. A total of 24 chemicals were accepted by the Lipinski's rules. We found a sole triterpenoid from ABBR via GC–MS, suggesting that might be a potent ligand to alleviate OP. Thereby, the 42 targets were associated with the triterpenoid; the 19 targets among them were connected to OP-targets (1426). The final 19 targets were related directly to 8 signaling pathways on STRING database. On Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and a key signaling pathway (PPAR signaling pathway), four key targets (PPARA, PPARD, FABP3, and FABP4) and a key compound (Methyl 3β-hydroxyolean-18-en-28-oate) were selected via MD. Collectively, the triterpenoid from ABBR might have potent anti-osteoporotic efficacy by activating PPARA, PPARD, FABP3, and FABP4 on PPAR signaling pathway.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03362-5.

Keywords: Acyranthes bidentata Blume root, Osteoporosis, Network pharmacology, PPAR signaling pathway, Methyl 3β-hydroxyolean-18-en-28-oate

Introduction

Osteoporosis (OP), a persistent inflammatory disease, brings bone loss and fractures as well as suppresses bone turnover (Montalcini et al. 2013; Stagi et al. 2014). In essential, OP is an abnormal metabolic bone syndrome defined as a reduction in bone density and breakdown of osseous tissue, which leads to bone frailty (Teng et al. 2020). According to a report, one in two Americans more than 50 years old is exposed to risk of progressive osteoporosis of the hip; furthermore, it will be risk at any sites in the bones ((US) 2004). Inflammation is a main factor to induce clinical symptoms such as back pain and joint pain in OP patients; anti-inflammation is a promising therapeutic approach (Paolucci et al. 2016; Rao et al. 2018). Currently, anti-osteoporosis drugs are categorized into two classes: nutritional supplements to maintain the level of calcium in the body and antiresorptive agents to enhance bone strength (Pavone et al. 2017). On the other hand, herbal medicines play important roles to treat OP, due to their efficacy, low side effects, and relatively low price (Abd Jalil et al. 2012). Among a plethora of herbal plants, Korean folk remedies have been noted the therapeutic efficacy of Acyranthes bidentata Blume root (ABBR) on OP. The hot water extraction of ABBR significantly elevated calcium accumulation in MC3T3-E1 cell (osteoblast cell) induced by chemicals (Kim et al. 2015). Calcium makes effect on preventing bone fracture associated with OP, especially occurred in postmenopausal women (Sunyecz 2008). It elicits that AABR has potent efficacy to alleviate OP.

Most recently, it was reported that rats-treated ABBR extract for 16 weeks improves bone mineral density (BMD) and quality on postmenopausal osteoporosis (Zhang et al. 2012). Another report demonstrates that ABBR extract leads to alkaline phosphatase stimulation, collagen synthesis elevation, osteocalcin production increase, and mineralization in osteoblastic MC3T3-E1 cells (P value < 0.05) (Suh et al. 2014). In particular, five new triterpenoids from ABBR might be preventive efficacy of bone degradation by inhibiting the osteoclast-like multinucleated cells (OCLs) (Li et al. 2005). Therefore, the research on active triterpenoids from ABBR extract should be implemented to strengthen therapeutic evidence for treatment of OP patients.

Network pharmacology is a systemic approach method to understand networks between compound and target, especially which can be used efficiently in searching of herb-target interaction (Liang et al. 2016; Oh et al. 2021b). Additionally, network pharmacology can decode the complex mechanism of compound–target relationship, especially via pathway enrichment analysis (Liu et al. 2018). Up to date, network pharmacology has been utilized to find active compounds in Traditional Chinese Medicine (TCM) against diseases (Zhou et al. 2020).

In this study, network pharmacology was utilized to investigate targets and signaling pathways on a triterpenoid against OP. Firstly, chemical compounds from ABBR methanolic extract are identified by GC–MS. Secondly, a sole triterpenoid was selected by GC–MS analysis. Thirdly, targets associated with the triterpenoid and OP-related targets are retrieved via pubic bioinformatics (SEA and STP). Fifthly, the final overlapping genes were identified, which are constructed, visualized, and analyzed by RStudio software. Then, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis of the overlapping genes was implemented to understand pharmacological mechanism of ABBR against OP. Finally, molecular docking (MD) was carried out to evaluate affinity of the triterpenoid target(s), compared with positive ligands. The work diagram is shown in Fig. 1.

Fig. 1.

Fig. 1

Open in a new tab

Workflow diagram of this study

Materials and methods

ABBR collection and identification

The ABBR was collected from Young-chun (Latitude: 35.983056, Longitude: 128. 947122), Kyeongsang-bukdo, Republic of Korea, in September 2020. A voucher number (UNB-051) has been deposited at the Department of Bio-Health Convergence, and the material can be only used as research.

ABBR preparation and extraction

The ABBR was dried in a sun-screened area at room temperature (20–22 °) for 7 days, and the dried roots were powdered using an electric blender. Around 40 g of ABBR powder was soaked in 700 ml of 100% methanol (Daejung, Korea) for 5 days and repeated 3 times to collect extraction. We used a vacuum evaporator (IKA- RV8, Staufen city, Germany) to evaporate ethanol. Then, the solvent was removed under a digital heating bath (IKA-HB10, Staufen city, Germany) at 40 °C.

GC–MS analysis condition

The GC–MS analysis was conducted via Agilent 7890A instrument which used a DB-5 (30 m × 0.25 mm × 0.25 μm) capillary column for its analysis. Firstly, the instrument was retained at a temperature of 100 °C for 2.1 min, which was rose to 300 °C at the rate of 25 °C/min and maintained for 20 min. Injection port temperature and helium flow rate were set as 250 °C and 1.5 ml/min, respectively. The ionization voltage was 70 eV. The samples are injected at 10:1 with the split mode; its MS scan range was configured at 35–900 (m/z). The fragmentation of detected compounds was compared with W8N05ST Library MS database.

The relative integral value (peak area) of each compound was calculated according to the ratio of each compound. The concept of integration was configured by the ChemStation algorithms (Oh et al. 2021a).

Identification of a triterpenoid from ABBR

The chemical compounds from ABBR were identified by utilizing GC–MS analysis. The PubChem (https://pubchem.ncbi.nlm.nih.gov/) was utilized to select the SMILES (Simplified Molecular Input Line Entry System).

Targets associated with a triterpenoid or osteoporosis

Targets associated with the triterpenoid were identified through both Similarity Ensemble Approach (SEA) (http://sea.bkslab.org/) (Keiser et al. 2007) and SwissTargetPrediction (STP) (http://www.swisstargetprediction.ch/) (Daina et al. 2019) with “Homo Sapiens” mode, in the form of SMILES. The osteoporosis (OP)-related targets on human were obtained from DisGeNET (https://www.disgenet.org/) Therapeutic Target Database (TTD) (http://db.idrblab.net/ttd/) and Online Mendelian Inheritance in Man (OMIM) (https://www.omim.org/). Through Venn diagram tool (VENNY 2.1), we identified significant candidate targets by selecting the intersecting targets between SEA and STP. The overlapping targets between a triterpenoid and OP targets are illustrated by VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/).

Construction of bubble chart based on Rich Factor

The bubble chart was illustrated by RStudio software, which was depicted by Rich Factor defined as the proportion of the Differentially Expressed Genes (DEGs) number and the number of targets annotated in each signaling pathway (Zambounis et al. 2020).

Construction of signaling pathways–targets–compound network

The signaling pathways–targets–compound (STC) network was constructed as a size map based on the degree of value. In the STC network, pink circles (nodes) represented signaling pathways; yellow rectangles (nodes) represented targets. The size of the yellow rectangle stood for the number of connectivity to signaling pathways. The merged networks were constructed using RPackage (Bates et al. 2015).

Identification of targets from KEGG pathway database

KEGG pathway database is utilized to identify the location of the uppermost targets on a key signaling pathway. KEGG pathway shows the relationships between targets and signaling pathways. The uppermost targets were highlighted with yellow color on a KEGG pathway.

Preparation for molecular docking (MD) of targets

Two targets of a key signaling pathway, i.e., RELA (PDB ID: 2O61; Resolution: 2.80 Å) and IL1B (PDB ID: 4DEP; Resolution: 3.10 Å) were identified on RCSB PDB (https://www.rcsb.org/) via STRING (https://string-db.org/) (Szklarczyk et al. 2019). The two targets selected as.pdb were converted into.pdbqt format via Autodock (http://autodock.scripps.edu/) (Morris et al. 2009).

Preparation for MD of positive standard ligands

The number of 10 positive standard ligands on RELA inhibitors, i.e., triptolide (PubChem ID: 129010078), tenalisib (PubChem ID: 86291103), schisantherin A (PubChem ID: 151529), maslinic acid (PubChem ID: 73659), magniferin (PubChem ID: 5281647), licochalone D (PubChem ID: 10473311), IAXO-102 (PubChem ID: 25222900), dehydrocostus lactone (PubChem ID: 73174), curcumenol (PubChem ID: 167812), adjudin (PubChem ID: 9819086),was selected to verify each docking score.

Preparation for MD of a ligand molecule

Methyl 3β-hydroxyolean-18-en-28-oate (PubChem ID: 609117) from ABBR was converted.sdf from PubChem into.pdb format using Pymol, and the ligand molecule was converted into.pdbqt format through Autodock.

Ligand–protein docking condition

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 (Khanal et al. 2020). The active site's grid box size was x = 20.973 Å, y = 25.96 Å and z = 41.239 Å. The LigPlot + v.2.2 (https://www.ebi.ac.uk/thornton-srv/software/LigPlus/) was employed to identify the 2D binding interactions between ligands and targets.

Toxicological properties prediction by admetSAR

Toxicological properties of a triterpenoid were determined using the admetSAR database (http://lmmd.ecust.edu.cn/admetsar1/predict/) (Yang et al. 2019) and ADMETlab 2.0 (Xiong et al. 2021) because toxicity is a critical element to develop new drugs. Hence, Ames toxicity, carcinogenic properties, acute oral toxicity, and rat acute toxicity were predicted by admetSAR. In addition, Human Ether-a-go-go-Related Gene (hERG) blockers, Drug-Induced Liver Injury (DILI), skin sensitization, eye corrosion, and eye irritation were assessed by ADMETlab 2.0

Results

Physicochemical properties of chemical compounds from ABBR

A total of 24 chemical compounds in ABBR were identified by GC–MS analysis (Fig. 2), and name of compounds, retention time, peak area (%), PubChem ID are exhibited in Table 1.

Fig. 2.

Fig. 2

Open in a new tab

GC–MS chromatogram of ABBR methanolic extract

Table 1.

A list of detected 24 compounds from ABBR through GC–MS

No Compounds PubChem ID RT (mins) Area (%) Classification
1 1-Aminopropan-2-ol 4 3.395 1.06 Amines
2 5-Methylfurfural 12097 3.491 1.15 Carbonyl compounds
3 2,5-Dimethylpiperazine 7816 4.058 4.05 Piperazines
4 Pentanal 8063 4.173, 4.27 9.15 Carbonyl compounds
5 N,N,N',N'-Tetramethylethylenediamine 8037 4.558 2.17 Amines
6 N,N-Dimethylglycine 673 4.606 1.46 Amino acids, peptides, and analogues
7 4H-Pyran-4-one, 2,3-dihydro-3,5-dihydroxy-6-methyl- 119838 4.779 14.58 Pyranones and derivatives
8 5-Hydroxymethylfurfural 237332 5.385 24.4 Carbonyl compounds
9 Deanol 7902 5.885 1.34 Amines
10 5-Ethylpyrrolidin-2-one 13301951 6.404 2.55 Pyrrolidones
11 Thiophene 8030 6.462 4.84 Heteroaromatic compounds
12 N-Methylhomopiperazine 228349 7.02 6.38 1,4-diazepanes
13 cis-9-Amino-9-azabicyclo [6.1.0]-(4Z)-nonene 5365250 7.193 1.78 Aziridines
14 2-Aminobutan-1-ol 22129 7.241 0.91 Amines
15 3-Hydroxy-1-methylpiperidine 98016 7.308 1.76 Piperidines
16 Betaine hydrochloride 11545 7.712, 7.895,8.731, 9.395, 11.664 4.81 Amino acids, peptides, and analogues
17 1-Propanone, 1-(1-adamantyl)-3-dimethylamino- 546927 7.779 3.52 Carbonyl compounds
18 Palmitic acid 985 8.914 3.22 Fatty acids
19 Ethyl palmitate 12366 8.991 1.88 Fatty acid esters
20 Linoleic acid 5280450 9.577 2.01 Lineolic acids and derivatives
21 9,12-Octadecadienoic acid, ethyl ester 5365672 9.635 4.31 Lineolic acids and derivatives
22 3-(Dimethylamino)propanenitrile 15615 10.193 0.34 Amines
23 N,N-Diethylbutane-1,4-diamine 117976 10.895 1.29 Amines
24 Methyl 3β-hydroxyolean-18-en-28-oate★ 609117 20.971 1.06 Triterpenoids
Open in a new tab

★: A significant compound in ABBR against OP

Target network construction of a triterpenoid from ABBR

A sole triterpenoid out of 24 chemical compounds; Methyl 3β-hydroxyolean-18-en-28-oate (PubChem ID: 609117) (Fig. 3) was identified by GC–MS. The number of 42 targets in both SEA and STP was associated directly with methyl 3β-hydroxyolean-18-en-28-oate (PubChem ID: 609117) (Fig. 4) (Supplementary Table S1).

Fig. 3.

Fig. 3

Open in a new tab

2D Structure of Methyl 3β-hydroxyolean-18-en-28-oate

Fig. 4.

Fig. 4

Open in a new tab

Number of 42 targets related to Methyl 3β-hydroxyolean-18-en-28-oate

Final overlapping target proteins between 42 targets and OP-related targets

A total of 1,426 target proteins associated with OP were identified by DisGeNET, and OMIM databases (Supplementary Table S2). Venn diagram exhibited that 19 final overlapping target proteins were identified between 1,426 OP-related and 42 overlapping target proteins (Fig. 5).

Fig. 5.

Fig. 5

Open in a new tab

Number of 19 targets between the 42 targets (Fig. 4) and 1426 targets (OP-related targets)

Potential 8 signaling pathways of methyl 3β-hydroxyolean-18-en-28-oate against OP

The KEGG pathway enrichment analysis suggested that 19 overlapping target proteins were significantly connected to 8 signaling pathways (False discovery rate < 0.05). A bubble chart of the 8 signaling pathways suggested that these signaling pathways might be the significant mechanisms of ABBR against OP (Fig. 6).

Fig. 6.

Fig. 6

Open in a new tab

Bubble chart of 8 signaling pathways associated with occurrence and development of OP

STC network of methyl 3β-hydroxyolean-18-en-28-oate against OP

STC network of methyl 3β-hydroxyolean-18-en-28-oate is illustrated in Fig. 7. The components consist of 8 signaling pathways, 10 targets, and 1 compound (19 nodes, 34 edges). The nodes stand for a total number of components: signaling pathway–target–compound (STC). The edges stood for a total number of relationships among the three components. The STC network suggested that the three components were associated directly with the therapeutic efficacy against OP.

Fig. 7.

Fig. 7

Open in a new tab

STC networks. Pink circle: signaling pathways; Yellow square: targets

Identification of a hub signaling pathway of ABBR against OP

PPAR signaling pathway with the highest rich factor among 8 signaling pathways was considered as a hub signaling pathway. The target proteins associated with the PPAR signaling pathway were PPARA, PPARG, FABP3, and FABP4 (Table 2).

Table 2.

Targets in 8 signaling pathways related to OP

KEGG ID Target genes False discovery rate
hsa03320:PPAR signaling pathway PPARA,PPARG,FABP3,FABP4 0.00014
hsa04917:Prolactin signaling pathway RELA,ESR1,CYP17A1 0.00330
hsa04657:IL-17 signaling pathway RELA,IL1B,PTGS2 0.00460
hsa04064:NF-kappa B signaling pathway RELA,IL1B,PTGS2 0.00550
hsa04625:C-type lectin receptor signaling pathway RELA,IL1B,PTGS2 0.00550
hsa04920:Adipocytokine signaling pathway RELA,PPARA 0.04200
hsa04668:TNF signaling pathway RELA,IL1B,PTGS2 0.00620
hsa04024:cAMP signaling pathway RELA,PPARA,PTGER2 0.02650
Open in a new tab

Comparative analysis of MD against positive ligands on PPAR signaling pathway

Methyl 3β-hydroxyolean-18-en-28-oate bound more stable to each target (PPARA, PPARG, FABP3, FABP4) related to PPAR signaling pathway. The molecular docking results exhibited that the docking scores of Methyl 3β-hydroxyolean-18-en-28-oate to PPARA, PPARG, FABP3, and FABP4 were − 14.2 kcal/mol, − 13.7 kcal/, − 21.4 kcal/mol, and − 13.3 kcal/mol, respectively, suggesting that methyl 3β-hydroxyolean-18-en-28-oate could exert a stable binding effect with the four key proteins. The docking scores are enlisted in Table 3. In parallel, methyl 3β-hydroxyolean-18-en-28-oate established hydrogen bonding with Met220 and Met320 amino acids; hydrophobic bonding with Phe218, Asn219, Glu286, Thr279, Tyr334, Ala333, Il2901, Val332, Leu331, and Val324 of PPARA (PDB ID: 3SP6). On PPARG (PDB ID: 3E00), methyl 3β-hydroxyolean-18-en-28-oate indicated only hydrophobic bonding with Ile296, Ile326, Phe226, Met329, Pro227, Leu228, Glu343, Leu333, Ala292, and Glu295. Likewise, both on FABP3 (PDB ID: 5HZ9) and FABP4 (PDB ID: 3P6D) had no hydrogen bonding, showing only hydrophobic interactions on methyl 3β-hydroxyolean-18-en-28-oate.

Table 3.

MD of Methyl 3β-hydroxyolean-18-en-28-oate on PPAR signaling pathway and comparison with positive ligands

Protein Ligand PubChem ID Binding energy(kcal/mol) Grid box Hydrogen bond interactions Hydrophobic interactions
Center Dimension Amino acid residue Amino acid residue

PPARA

(PDB ID: 3SP6)

 Methyl 3β-hydroxyolean-18-en-28-oate 609117 − 14.2 x = 8.006 Size_x = 40 Met220, Met320 Phe218, Asn219, Glu286
y = − 0.459 Size_y = 40 Thr279, Tyr334, Ala333
z = 23.392 Size_z = 40 Il2901, Val332, Leu331
Val324
1)Clofibrate 2796 − 6.4 x = 8.006 Size_x = 40 Thr283 Ala333, Tyr334, Asn219
y = − 0.459 Size_y = 40 Met320, Leu321, Met220
z = 23.392 Size_z = 40 Phe218, Val332, Val324
Thr279, Il2901
2)Gemfibrozil 3463 − 6.3 x = 8.006 Size_x = 40 Tyr468 Lys448, Leu456, Arg465
y = − 0.459 Size_y = 40 Gln442, Ala444, Gln445
z = 23.392 Size_z = 40 Val444, Tyr464
3)Ciprofibrate 2763 − 5.4 x = 8.006 Size_x = 40 Ala333, Thr279 Lys257, Cys278, Tyr334
y = − 0.459 Size_y = 40 Il2901, Cys275, Val255
z = 23.392 Size_z = 40 Leu258
4)Bezafibrate 39042 − 5.8 x = 8.006 Size_x = 40 Thr307, Ser688 Asn303, Glu462, Val306
y = − 0.459 Size_y = 40 Leu690, Lys310, Gly390
z = 23.392 Size_z = 40
5)Fenofibrate 3339 − 5.4 x = 8.006 Size_x = 40 N/A Ala431, Asp360, Pro357
y = − 0.459 Size_y = 40 Leu436, Glu439, Lys364
z = 23.392 Size_z = 40 Phe361, Asp432, Gln435
PPARG (PDB ID: 3E00) Methyl 3β-hydroxyolean-18-en-28-oate 609117 − 13.7 x = 2.075 Size_x = 40 N/A Ile296, Ile326, Phe226
y = 31.910 Size_y = 40 Met329, Pro227, Leu228
z = 18.503 Size_z = 40 Glu343, Leu333, Ala292
Glu295
6)Pioglitazone 4829 − 7.7 x = 2.075 Size_x = 40 Arg288 Ile326, Leu333, Met329
y = 31.910 Size_y = 40 Ala292, Ile341, Cys285
z = 18.503 Size_z = 40 Ser342, Glu343, Glu295
Leu228
7)Rosiglitazone 77999 − 7.4 x = 2.075 Size_x = 40 Tyr169 Glu351, Tyr189, Thr168
y = 31.910 Size_y = 40 Gln193, Leu167, Glu369
z = 18.503 Size_z = 40 Lys373, Val372, Arg350
Lys336, Tyr192, Asp337
8)Lobeglitazone 9826451 − 7.3 x = 2.075 Size_x = 40 Arg234 Val372, Asn375, Met334
y = 31.910 Size_y = 40 Val163, Lys230, Glu203
z = 18.503 Size_z = 40 Lys157, Val205, Arg164
Arg202, Asp166, Lys336
FABP3 (PDB ID: 5HZ9) Methyl 3β-hydroxyolean-18-en-28-oate 609117 − 21.4 x = − 1.215 Size_x = 40 N/A Phe28, Gln32, Ala29
y = 46.730 Size_y = 40 Lys22, Met36, Thr57
z = − 15.099 Size_z = 40
FABP4 (PDB ID: 3P6D) Methyl 3β-hydroxyolean-18-en-28-oate 609117 − 13.3 x = 7.693 Size_x = 40 N/A Lys105, Glu109, Lys107
y = 9.921 Size_y = 40 Val114, Glu116, Val90
z = 14.698 Size_z = 40
Open in a new tab

1),2),3),4),5): PPARA agonist. 6),7),8): PPARG agonist

The lower the docking score (the greater negative score), the better stable complex is between target protein and ligand. Additionally, clofibrate, gemfibrozil, ciprofibrate, bezafibrate, and fenofibrate as positive ligands were the main drugs for PPARA agonists (Han et al. 2017). Then, pioglitazone, rosiglitazone, and lobeglitazone as positive ligands were the key drugs for PPARG agonists (Lee et al. 2017). Studies have demonstrated that all PPAR agonists interrupt osteoclastogenesis related to bone loss (Kasonga et al. 2019; Dobson et al. 2020). We observed that methyl 3β-hydroxyolean-18-en-28-oate could bind more stably to PPARA, PPARG, FABP3, and FABP4 than positive ligands. The 3D and 2D diagrams of MD on selecting 4 target proteins with the stable biding are displayed in Fig. 8.

Fig. 8.

Fig. 8

Open in a new tab

MD of on Methyl 3β-hydroxyolean-18-en-28-oate on four targets of PPAR signaling pathway. A Methyl 3β-hydroxyolean-18-en-28-oate on PPARA (PDB ID: 3SP6). B Methyl 3β-hydroxyolean-18-en-28-oate on PPARG (PDB ID: 3E00). C Methyl 3β-hydroxyolean-18-en-28-oate on FABP3 (PDB ID: 5HZ9). D Methyl 3β-hydroxyolean-18-en-28-oate on FABP4 (PDB ID: 3P6D)

Toxicological propensity of methyl 3β-hydroxyolean-18-en-28-oate

In addition, toxicity of methyl 3β-hydroxyolean-18-en-28-oate was predicted by both admetSAR online tool and ADMETlab 2.0. Our result indicated that the active compound has no shown Ames toxicity, carcinogenic properties, acute oral toxicity, rat acute toxicity, Human Ether-a-go-go-Related Gene (hERG) blockers, Drug-Induced Liver Injury (DILI), skin sensitization, eye corrosion, and eye irritation properties (Table 4).

Table 4.

Toxicological propensity of Methyl 3β-hydroxyolean-18-en-28-oate

Parameters Compound name
Methyl 3β-hydroxyolean-18-en-28-oate
1) Ames Toxicity (AT) Non-Ames Toxic (NAT)
2) Carcinogens Non-Carcinogenic (NC)
3) Acute oral toxicity III (500 mg/kg > LD50 < 5000 mg/kg)
4) Rat acute toxicity Negative
5) Human Ether-a-go-go-Related Gene (hERG) blockers Negative
6) Drug-Induced Liver Injury (DILI) Negative
7) Skin sensitization Negative
8) Eye corrosion Negative
9) Eye irritation Negative
Open in a new tab

1) Ames mutagenicity: Salmonella mutagenicity assay

3) Category III means (500 mg/kg > LD50 < 5000 mg/kg)

5) Human Ether-a-go-go-Related Gene (hERG): encoding potassium ion (K +) channel

6) Drug-Induced Liver Injury (DILI): A liver injury induced from various drugs

Discussion

A bubble chart suggested that the pharmacological mechanisms of ABBR against OP were directly related to 8 signaling pathways. Among the 8 signaling pathways, PPAR signaling pathway was the highest rich factor, indicating that methyl 3β-hydroxyolean-18-en-28-oate might be an agonist on PPAR signaling pathway. The detailed underlying mechanism of methyl 3β-hydroxyolean-18-en-28-oate as a triterpenoid remains unknown. Previous studies suggested that the triterpenoid can be significant compounds of anti-OP (Hao et al. 2015; Deng et al. 2015). Furthermore, the triterpenoids are potent anti-inflammatory agents against diverse autoimmune diseases (Yin 2015). It implies that a triterpenoid from ABBR might be a key compound against OP.

The results of KEGG pathway enrichment analysis suggested that 8 signaling pathways were related directly to occurrence and progression of OP. The relationships of the 8 signaling pathways with OP were concisely discussed as follows. Peroxisome Proliferator-Activated Receptor (PPAR) signaling pathway: An animal experiment demonstrated that Peroxisome Proliferator-Activated Receptor Alpha (PPARA) activation in rodents after treating fibrates dampens the bone loss; furthermore, Peroxisome Proliferator-Activated Receptor Gamma (PPARG) plays crucial role in bone remodeling by accelerating the activity of osteoblasts for repair (Cao et al. 2015; Stechschulte et al. 2016).

A report shows that FABP3 increased the expression of PPARA, and FABP4 stabilizes PPARA, reporting that FABP4 binds to PPARA and improves its biological function by activating transcriptional function (Zhuang et al. 2021). Cyclic Adenosine MonoPhosphate (cAMP) signaling pathway: A report proved that inhibitors of the cyclic nucleotide phosphodiesterases (PDEs) which hydrolyze phosphodiester bond including cAMP have osteogenic effect to alleviate for post-menopausal osteoporosis (Porwal et al. 2021). It can be speculated that inhibition of cAMP signaling pathway is a potential mechanism to relieve OP. Nuclear Factor kappa-light-chain-enhancer of activated B (NF-kappa B) signaling pathway: A research demonstrated that inhibition of NF-kappa B has effects on improving bone regeneration, repair, and anti-inflammation to alleviate OP (Chang et al. 2013). C-type leptin receptor signaling pathway: The inhibition of leptin signaling pathway in mice increases in bone formation, suggesting that inhibitor of leptin might be a promising candidate against OP (Ducy et al. 2000). Interleukin-17 (IL-17) signaling pathway: A report shows that IL-17 expression levels in OP subjects elevated compared to health subjects; moreover, the absence of IL-17 in mice develops importantly more periosteal bone than normal-type mice in inflammatory condition (Shaw et al. 2016). Tumor Necrosis Factor (TNF) signaling pathway: A report demonstrated that anti-TNF could significant inhibit the development of postmenopausal osteoporosis by blocking the production of osteoclasts (Lu et al. 2020). Prolactin signaling pathway: The elevated prolactin is a direct negative point in OP patients, indicating that OP patients are exposed to the risk of OP (O’Keane 2008). Adipocytokine signaling pathway: A research reported that increased level of adipocytokines in postmenopausal women gives a clue the occurrence and development of OP fractures (Nakamura et al. 2020). It implies that dampening of adipocytokines might be a potential strategy to alleviate OP.

To be specific, we conducted the MD to evaluate the affinity between methyl 3β-hydroxyolean-18-en-28-oate and each target. Most recently, key promising agents for anti-tuberculosis, anti-cancer, and anti-dengue have been pioneered in silico test (Yang et al. 2019; Kumar Bhardwaj and Purohit 2021; Kumar et al. 2022b, a; Bhardwaj and Purohit 2022; Singh et al. 2022). Furthermore, underpinning mechanism of DNA clamp opening and selective inhibitors of cyclin-dependent kinase 5 (CDK 5) to be involved in diverse diseases can be revealed by computational approach (Kumar Bhardwaj et al 2022; Bhardwaj et al. 2022).

It has been suggested that computational analysis is efficient methodology to pre-screen before in vitro or in vivo experimentation. Thus, we utilized in silico test to uncover key components from ABBR against OP.

From the network pharmacology concept based on computational analysis, this study elaborates on targets, signaling pathways of a triterpenoid (methyl 3β-hydroxyolean-18-en-28-oate). The network pharmacology elucidates that methyl 3β-hydroxyolean-18-en-28-oate might activate PPAR signaling pathway against OP, providing scientific evidence for further experimental validation.

Conclusion

On network pharmacology, we identified interaction network of one compound (methyl 3β-hydroxyolean-18-en-28-oate)–multiple targets (PPARA, PPARG, FABP3, and FABP4) of ABBR on OP. Besides, MD also showed that methyl 3β-hydroxyolean-18-en-28-oate could dock more stably to PPARA, PPARG than existing drugs, suggesting that methyl 3β-hydroxyolean-18-en-28-oate might be a promising agent against OP, by activating PPAR signaling pathway. To sum up, we suggest that methyl 3β-hydroxyolean-18-en-28-oate might be a potent agonist to alleviate OP. However, this study is also required preclinical or clinical test to verify the pharmacological efficacy and our findings provide scientific evidence to reveal the therapeutic value of ABBR on OP.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 10 KB) (9.6KB, xlsx)
Supplementary file2 (XLSX 27 KB) (27.1KB, xlsx)

Acknowledgements

This research was supported by the Department of Bio-Health Convergence, Kangwon National University, Chuncheon 24341, Re- public of Korea.

Abbreviations

ABBR

Acyranthes bidentata Blume Root

BMD

Bone mineral density

cAMP

Cyclic adenosine monophosphate

DEGs

Differentially expressed genes

GC–MS

Gas chromatography-mass spectrum

IL-17

InterLeukin-17

KEGG

Kyoto encyclopedia of genes and genomes

MD

Molecular docking

NF-kappa B

Nuclear factor kappa-light-chain-enhancer of activated B

OCLs

Osteoclast-like multinucleated cells

OP

Osteoporosis

PPAR

Peroxisome proliferator-activated receptor

PPARA

Peroxisome proliferator-activated receptor alpha

PPARG

Peroxisome proliferator-activated receptor gamma

PDEs

PhosphoDiEsterases

SMILES

Simplified molecular input line entry system

SEA

Similarity ensemble approach

STP

SwissTargetPrediction

TCM

Traditional Chinese medicine

TNF

Tumor necrosis factor

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declarations

Conflict of interest

The authors have declared no conflict of interest. They have no known competing financial interests or personal relationships that could have appeared to influence the research reported in this publication.

Research involving human participants and/or animals

Not applicable to the current research as we have not used any human and/or animal subject.

Informed consent

Not applicable.

References

  1. Abd Jalil MA, Shuid AN, Muhammad N. Role of medicinal plants and natural products on osteoporotic fracture healing. Evid Based Complement Alternat Med. 2012 doi: 10.1155/2012/714512. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48. doi: 10.18637/JSS.V067.I01. [DOI] [Google Scholar]
  3. Bhardwaj VK, Purohit R. A lesson for the maestro of the replication fork: targeting the protein-binding interface of proliferating cell nuclear antigen for anticancer therapy. J Cell Biochem. 2022;123:1091–1102. doi: 10.1002/JCB.30265. [DOI] [PubMed] [Google Scholar]
  4. Bhardwaj VK, Oakley A, Purohit R. Mechanistic behavior and subtle key events during DNA clamp opening and closing in T4 bacteriophage. Int J Biol Macromol. 2022;208:11–19. doi: 10.1016/J.IJBIOMAC.2022.03.021. [DOI] [PubMed] [Google Scholar]
  5. Cao J, Ou G, Yang N, et al. Impact of targeted PPARγ disruption on bone remodeling. Mol Cell Endocrinol. 2015;410:27. doi: 10.1016/J.MCE.2015.01.045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Chang J, Liu F, Lee M, et al. NF-κB inhibits osteogenic differentiation of mesenchymal stem cells by promoting β-catenin degradation. Proc Natl Acad Sci. 2013;110:9469–9474. doi: 10.1073/PNAS.1300532110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Daina A, Michielin O, Zoete V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019;47:W357–W3664. doi: 10.1093/nar/gkz382. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Deng Y, Kang W, Zhao J, et al. Osteoprotective effect of echinocystic acid, a triterpone component from eclipta prostrata, in ovariectomy-induced osteoporotic rats. PLoS ONE. 2015 doi: 10.1371/JOURNAL.PONE.0136572. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Dobson PF, Dennis EP, Hipps D, et al. (2020) Mitochondrial dysfunction impairs osteogenesis, increases osteoclast activity, and accelerates age related bone loss. Sci Rep. 2020;10:1–14. doi: 10.1038/s41598-020-68566-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Ducy P, Amling M, Takeda S, et al. Leptin inhibits bone formation through a hypothalamic relay: a central control of bone mass. Cell. 2000;100:197–207. doi: 10.1016/S0092-8674(00)81558-5. [DOI] [PubMed] [Google Scholar]
  11. Han L, Shen W-J, Bittner S, et al. PPARs: regulators of metabolism and as therapeutic targets in cardiovascular disease. Part I: PPAR-α. Future Cardiol. 2017;13:259. doi: 10.2217/FCA-2016-0059. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Hao DC, Gu X-J, Xiao PG. Chemical and biological studies of Cimicifugeae pharmaceutical resources. Med Plants. 2015 doi: 10.1016/B978-0-08-100085-4.00008-6. [DOI] [Google Scholar]
  13. Kasonga A, Kruger MC, Coetzee M. Activation of PPARs modulates signalling pathways and expression of regulatory genes in osteoclasts derived from human CD14+ monocytes. Int J Mol Sci. 2019 doi: 10.3390/IJMS20071798. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Keiser MJ, Roth BL, Armbruster BN, et al. Relating protein pharmacology by ligand chemistry. Nat Biotechnol. 2007;25:197–206. doi: 10.1038/nbt1284. [DOI] [PubMed] [Google Scholar]
  15. Khanal P, Patil BM, Chand J, Naaz Y. Anthraquinone derivatives as an immune booster and their therapeutic option against COVID-19. Nat Prod Bioprospect. 2020;10:325–335. doi: 10.1007/s13659-020-00260-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Kim JS, Lee SW, Kim YO, et al. Effects of the hot water extract mixtures from Achyranthes bidentata Blume and Panax ginseng on osteoclast and osteoblast differentiation. Korean J Med Crop Sci. 2015;23:117–124. doi: 10.7783/KJMCS.2015.23.2.117. [DOI] [Google Scholar]
  17. Kumar S, Bhardwaj VK, Singh R, et al. Identification of acridinedione scaffolds as potential inhibitor of DENV-2 C protein: an in silico strategy to combat dengue. J Cell Biochem. 2022;123:935–946. doi: 10.1002/JCB.30237. [DOI] [PubMed] [Google Scholar]
  18. Kumar S, Bhardwaj VK, Singh R, et al. Evaluation of plant-derived semi-synthetic molecules against BRD3-BD2 protein: a computational strategy to combat breast cancer. Mol Syst Des Eng. 2022;7:381–391. doi: 10.1039/D1ME00183C. [DOI] [Google Scholar]
  19. Kumar Bhardwaj V, Purohit R. Taming the ringmaster of the genome (PCNA): phytomolecules for anticancer therapy against a potential non-oncogenic target. J Mol Liq. 2021;337:116437. doi: 10.1016/J.MOLLIQ.2021.116437. [DOI] [Google Scholar]
  20. Kumar Bhardwaj V, Das P, Purohit R. Identification and comparison of plant-derived scaffolds as selective CDK5 inhibitors against standard molecules: Insights from umbrella sampling simulations. J Mol Liq. 2022;348:118015. doi: 10.1016/J.MOLLIQ.2021.118015. [DOI] [Google Scholar]
  21. Lee MA, Tan L, Yang H, et al. Structures of PPARγ complexed with lobeglitazone and pioglitazone reveal key determinants for the recognition of antidiabetic drugs. Sci Rep. 2017 doi: 10.1038/S41598-017-17082-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Li JX, Hareyama T, Tezuka Y, et al. Five new oleanolic acid glycosides from Achyranthes bidentata with inhibitory activity on osteoclast formation. Planta Med. 2005;71:673–679. doi: 10.1055/s-2005-871275. [DOI] [PubMed] [Google Scholar]
  23. Liang H, Ruan H, Ouyang Q, Lai L. Herb-target interaction network analysis helps to disclose molecular mechanism of traditional Chinese medicine. Sci Rep. 2016;6:1–10. doi: 10.1038/srep36767. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Liu L, Du B, Zhang H, et al. A network pharmacology approach to explore the mechanisms of Erxian decoction in polycystic ovary syndrome. Chin Med (UK) 2018;13:46. doi: 10.1186/s13020-018-0201-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Lu J, Zhou Z, Ma J, et al. Tumour necrosis factor-α promotes BMHSC differentiation by increasing P2X7 receptor in oestrogen-deficient osteoporosis. J Cell Mol Med. 2020;24:14316–14324. doi: 10.1111/JCMM.16048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Montalcini T, Romeo S, Ferro Y, et al. Osteoporosis in chronic inflammatory disease: the role of malnutrition. Endocrine. 2013;43:59–64. doi: 10.1007/s12020-012-9813-x. [DOI] [PubMed] [Google Scholar]
  27. Morris GM, Ruth H, Lindstrom W, et al. Software news and updates AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem. 2009;30:2785–2791. doi: 10.1002/jcc.21256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Nakamura Y, Nakano M, Suzuki T, Sato J, Kato H, Takahashi J, Shiraki M. Two adipocytokines, leptin and adiponectin, independently predict osteoporotic fracture risk at different bone sites in postmenopausal women. Bone. 2020 doi: 10.1016/J.BONE.2020.115404. [DOI] [PubMed] [Google Scholar]
  29. O’Keane V. Antipsychotic-induced hyperprolactinaemia, hypogonadism and osteoporosis in the treatment of schizophrenia. J Psychopharmacol. 2008;22:70–75. doi: 10.1177/0269881107088439. [DOI] [PubMed] [Google Scholar]
  30. Oh KK, Adnan M, Cho DH. Active ingredients and mechanisms of Phellinus linteus (grown on Rosa multiflora) for alleviation of Type 2 diabetes mellitus through network pharmacology. Gene. 2021;768:145320. doi: 10.1016/j.gene.2020.145320. [DOI] [PubMed] [Google Scholar]
  31. Oh KK, Adnan M, Ju I, Cho DH. A network pharmacology study on main chemical compounds from Hibiscus cannabinus L. leaves. RSC Adv. 2021;11:11062–11082. doi: 10.1039/d0ra10932k. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Paolucci T, Saraceni VM, Piccinini G. Management of chronic pain in osteoporosis: challenges and solutions. J Pain Res. 2016;9:177–186. doi: 10.2147/JPR.S83574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Pavone V, Testa G, Giardina SMC, et al. Pharmacological therapy of osteoporosis: a systematic current review of literature. Front Pharmacol. 2017 doi: 10.3389/fphar.2017.00803. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Porwal K, Pal S, Bhagwati S, et al. Therapeutic potential of phosphodiesterase inhibitors in the treatment of osteoporosis: scopes for therapeutic repurposing and discovery of new oral osteoanabolic drugs. Eur J Pharmacol. 2021;899:174015. doi: 10.1016/J.EJPHAR.2021.174015. [DOI] [PubMed] [Google Scholar]
  35. Rao SS, Hu Y, Xie PL, et al. Omentin-1 prevents inflammation-induced osteoporosis by downregulating the pro-inflammatory cytokines. Bone Res. 2018;6:1–12. doi: 10.1038/s41413-018-0012-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Shaw AT, Maeda Y. Gravallese EM (2016) IL-17A deficiency promotes periosteal bone formation in a model of inflammatory arthritis. Arthritis Res Ther. 2016;18:1–10. doi: 10.1186/S13075-016-0998-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Singh R, Kumar S, Bhardwaj VK, Purohit R. Screening and reckoning of potential therapeutic agents against DprE1 protein of Mycobacterium tuberculosis. J Mol Liq. 2022;358:119101. doi: 10.1016/J.MOLLIQ.2022.119101. [DOI] [Google Scholar]
  38. Stagi S, Cavalli L, Seminara S, et al. The ever-expanding conundrum of primary osteoporosis: aetiopathogenesis, diagnosis, and treatment. Ital J Pediatr. 2014;40:55. doi: 10.1186/1824-7288-40-55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Stechschulte LA, Czernik PJ, Rotter ZC, et al. PPARG post-translational modifications regulate bone formation and bone resorption. EBioMedicine. 2016;10:174. doi: 10.1016/J.EBIOM.2016.06.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Suh KS, Lee YS, Choi EM. The protective effects of Achyranthes bidentata root extract on the antimycin A induced damage of osteoblastic MC3T3-E1 cells. Cytotechnology. 2014;66:925–935. doi: 10.1007/s10616-013-9645-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Sunyecz JA. The use of calcium and vitamin D in the management of osteoporosis. Ther Clin Risk Manag. 2008;4:827. doi: 10.2147/TCRM.S3552. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–D613. doi: 10.1093/nar/gky1131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Teng Z, Zhu Y, Zhang X, et al. Osteoporosis is characterized by altered expression of exosomal long non-coding RNAs. Front Genet. 2020;11:566959. doi: 10.3389/fgene.2020.566959. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. (US) O of the SG (2004) The frequency of bone disease
  45. Xiong G, Wu Z, Yi J, et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 2021;49:W5–W14. doi: 10.1093/NAR/GKAB255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Yang H, Lou C, Sun L, et al. admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics. 2019;35:1067–1069. doi: 10.1093/BIOINFORMATICS/BTY707. [DOI] [PubMed] [Google Scholar]
  47. Yin M-C. Inhibitory effects and actions of pentacyclic triterpenes upon glycation. Biomedicine. 2015;5:1–8. doi: 10.7603/S40681-015-0013-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Zambounis A, Ganopoulos I, Valasiadis D, et al. RNA sequencing-based transcriptome analysis of kiwifruit infected by Botrytis cinerea. Physiol Mol Plant Pathol. 2020 doi: 10.1016/j.pmpp.2020.101514. [DOI] [Google Scholar]
  49. Zhang R, Hu SJ, Li C, et al. Achyranthes bidentata root extract prevent OVX-induced osteoporosis in rats. J Ethnopharmacol. 2012;139:12–18. doi: 10.1016/j.jep.2011.05.034. [DOI] [PubMed] [Google Scholar]
  50. Zhou Z, Chen B, Chen S, et al. Applications of network pharmacology in traditional Chinese medicine research. Evid Based Complement Alternat Med. 2020 doi: 10.1155/2020/1646905. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Zhuang L, Mao Y, Liu Z, et al. FABP3 deficiency exacerbates metabolic derangement in cardiac hypertrophy and heart failure via PPARα pathway. Front Cardiovasc Med. 2021 doi: 10.3389/FCVM.2021.722908. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (XLSX 10 KB) (9.6KB, xlsx)
Supplementary file2 (XLSX 27 KB) (27.1KB, xlsx)

Articles from 3 Biotech are provided here courtesy of Springer

RESOURCES