Network pharmacology combined with molecular docking to explore the anti-osteoporosis mechanisms of β-ecdysone derived from medicinal plants

Bin Zeng; Xudong Wu; Wenqing Liang; Xiaogang Huang

doi:10.1515/chem-2022-0262

Article Open Access

Network pharmacology combined with molecular docking to explore the anti-osteoporosis mechanisms of β-ecdysone derived from medicinal plants

Bin Zeng , Xudong Wu , Wenqing Liang and Xiaogang Huang

Published/Copyright: December 31, 2022

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information Explore this Subject

From the journal Open Chemistry Volume 20 Issue 1

Abstract

β-Ecdysone is a phytosteroid derived from multifarious medicinal plants, such as Achyranthes root (Achyranthes bidentata) and Tinospora cordifolia, possessing the potential anti-osteoporosis effect. However, the underlying mechanisms for β-ecdysone treating osteoporosis remain unclear. This study aims to explore the molecular mechanisms of β-ecdysone against osteoporosis by network pharmacology and molecular docking. First, the potential targets of β-ecdysone and osteoporosis were predicted by public databases. Protein interaction and functional enrichment analyses of potential targets were performed using the STRING and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases. Finally, hub targets were identified from network pharmacology, and their interaction with β-ecdysone was validated by molecular docking. Results showed that 47 potential targets were related to the mechanisms of β-ecdysone treating osteoporosis. Enrichment analyses revealed that the potential targets were mainly associated with steroid biosynthetic and metabolic processes, as well as HIF-1 and estrogen signaling pathways. By protein–protein interaction network analysis, top 10 hub targets were screened, including TNF, ALB, SRC, STAT3, MAPK3, ESR1, PPARG, CASP3, TLR4, and NR3C1. Molecular docking showed that β-ecdysone had good affinity with TLR4, TNF, and ESR1. Therefore, β-ecdysone might exert therapeutic effect on osteoporosis development via targeting TLR4, TNF, and ESR1 and regulating HIF-1 and estrogen pathways.

Keywords: β-ecdysone; osteoporosis; network pharmacology; molecular docking; functional enrichment analysis

1 Introduction

Osteoporosis is a common metabolic bone disease characterized by decreased bone mineral density and impaired bone microarchitecture, leading to the increases in bone fragility and fracture risk [1]. Approximately 200 million people suffer from osteoporosis worldwide [2]. Osteoporosis is common in old people and postmenopausal women, which significantly reduces the quality of life of patients [3]. The incidence of osteoporosis increases with age, and the prevalence is increasing with the aging of global population [4]. Moreover, an estimated 33% of women and 20% of men will develop osteoporotic fractures, especially in the spine, hip, and forearm [5,6]. Osteoporosis-related fractures are revealed as a substantial public health burden in China [7]. Therefore, early diagnosis and prevention of osteoporosis will decrease the risk of fragility fracture.

The current treatments for osteoporosis are mainly divided into anti-resorptive agents and anabolic ones. Anti-resorptive drugs, such as bisphosphonates, can reduce bone resorption, while anabolic drugs, like parathyroid hormone analogs, stimulate bone formation [8,9]. β-Ecdysone is a phytosteroid with selective estrogen receptor modulation that can be extracted and purified from multifarious medicinal plants, such as Achyranthes root (Achyranthes bidentata) and Tinospora cordifolia [10,11]. A. bidentata has been widely used in clinical practice to treat bone disease for thousands of years. The root extract of A. bidentata reportedly improves bone biomechanical quality and trabecular microarchitecture in postmenopausal osteoporosis [12]. T. cordifolia is an ayurvedic medicinal plant distributed throughout the Indian subcontinent and China [13]. T. cordifolia is an effective remedy to rheumatoid arthritis and diseases of musculoskeletal system, which stimulates osteogenesis and has been a potential anti-osteoporotic agent [14]. As a main active compound of A. bidentata and T. cordifolia, β-ecdysone possesses numerous pharmacological activities in protein synthesis, immunologic modulation, carbohydrate and lipid metabolism, as well as alleviating neurological disorders [15,16]. β-Ecdysone also has exhibited a variety of biological functions to decrease glucocorticoid-induced osteoblast apoptosis and alleviate ovariectomy-evoked bone loss and chondrocyte dysfunction [15,17,18]. β-Ecdysone reportedly protects against IL-1β-induced apoptosis and inflammation in chondrocytes [19]. Accumulating studies confirm that β-ecdysone plays a crucial part in osteoprotective role of T. cordifolia and has great potential as a promising anti-osteoporotic drug [20,21]. Gao et al. revealed that β-ecdysone can mitigate osteoporosis symptoms without obvious side effects [22]. However, the underlying mechanisms of β-ecdysone in preventing osteoporosis are largely unknown. Herein, we focused on investigating the potential molecular mechanisms of β-ecdysone as a targeted therapeutic agent for osteoporosis.

Network pharmacology has been widely used to explore the regulatory mechanisms of therapeutic drugs in diseases [23,24]. In this study, we performed network pharmacology and molecular docking analysis to investigate the possible molecular mechanism of β-ecdysone in the treatment of osteoporosis.

2 Materials and methods

2.1 Screening of osteoporosis-related genes

Genes related to osteoporosis were retrieved from the Online Mendelian Inheritance in Man (OMIM) database (https://www.omim.org/) and Malacard module of Genecard database [25] with the keyword of “osteoporosis.” After deleting duplicates, the osteoporosis-related genes screened from the two databases were combined for subsequent analysis.

2.2 Identification of β-ecdysone target genes

The target genes of β-ecdysone were predicted using the pharmMapper database (http://www.lilab-ecust.cn/pharmmapper/). The Human Protein Targets Only (v2010, 2241) was selected as the target gene set, and the top 300 Reserved Matched Targets were obtained. The target genes of β-ecdysone were then identified with Norm Fit ≥0.4645 and normalized to the NCBI Gene (https://www.ncbi.nlm.nih.gov/gene/) and the universal protein resource (UniProt) databases (https://www.uniprot.org/) [26]. Moreover, the β-ecdysone target genes were extracted with P value ≠ 0 in SwissTargetPrediction database (http://swisstargetprediction.ch/) and probability value = 50% in SuperPred database [27]. After deleting duplicates, the β-ecdysone target genes from the three databases were combined for subsequent analysis.

The intersection targets of osteoporosis and β-ecdysone were obtained by a Venn diagram.

2.3 Functional enrichment analysis

To reveal the function of potential targets in β-ecdysone treating osteoporosis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using R clusterProfiler package (v3.15.4). GO functions includes molecular function (MF), biological process (BP), and cellular component (CC) categories. The significantly enriched GO functions and pathways were screened using hypergeometric distribution model. The P value was calculated as follows:

P = 1 − ∑ i = 0 k − 1 ( M i ) ( N − M n − i ) ( N n ) ,

where N is the total number of genes, M is the number of genes enriched in a GO term or KEGG pathway, n is the number of β-ecdysone target gene, and k is the number of common genes.

The P value was adjusted by Bonferroni method, and the adjusted P value <0.05 was considered statistically significant.

2.4 Construction of protein–protein interaction (PPI) network

Based on the information of STRING databases (https://cn.string-db.org/), the PPI network was constructed for osteoporosis-related genes targeted by β-ecdysone, with the condition of confidence score >0.9. PPI network was visualized using Cytoscape software (v3.9.0). The top 10 hub targets with higher degree in the PPI network were identified using cytoscapehub plug-in.

2.5 Molecular docking analysis

The chemical structure of β-ecdysone was downloaded from Zinc website as mol2 format file. The chemical structure of β-ecdysone was then imported into AutoDockTools (v1.5.6) software to add hydrogen atoms and charge operations and then saved in pdbqt format. The 3D structure of the top 10 hub proteins in the PPI network was downloaded from RCSB PDB database (https://www.rcsb.org/) as PDB format file. The dehydration of the receptor protein was conducted using PyMol (v2.3.0) software. The proteins were imported into AutoDockTools (v1.5.6) software for hydrogenation and charge calculation and saved in the pdbqt format. The original protein ligand was set as the center of the docking box. If the receptor protein did not have the original ligand, the docking region was determined according to the key amino acid residues near the active pocket of the receptor protein reported in previous studies or analyzed using fPocket, an online pocket parameter prediction website. Finally, Autodock Vina (v1.1.2) [28] was used to dock the receptor protein with β-ecdysone. The docking results were analyzed and visualized using PyMOL (v2.3.0) software. The binding energy value (affinity) was used to evaluate the interaction between β-ecdysone and hub proteins. The lower the binding energy value, the stronger the binding of β-ecdysone to the target protein was. The cutoff value was affinity <−5.0 kcal/mol.

3 Results

3.1 Potential targets of β-ecdysone treating osteoporosis

A total of 798 and 269 genes associated with osteoporosis were retrieved from the OMIM and Genecards databases, respectively. Then, 981 osteoporosis-related genes were obtained by deleting duplicates. In addition, 146, 71, and 139 target genes of β-ecdysone were predicted using pharmMapper, SwissTargetPrediction, and Superpred databases, respectively. Then, 320 β-ecdysone target genes were screened by deleting duplicates. By further intersection analysis of the osteoporosis and β-ecdysone target genes, 47 potential targets in β-ecdysone treating osteoporosis were identified (Table 1).

Table 1

The osteoporosis-related genes targeted by β-ecdysone

ID	Name	ID	Name	ID	Name	ID	Name
213	ALB	1588	CYP19A1	4780	NFE2L2	1901	S1PR1
367	AR	1576	CYP3A4	4790	NFKB1	5054	SERPINE1
650	BMP2	1803	DPP4	4846	NOS3	6462	SHBG
715	C1R	2099	ESR1	2908	NR3C1	6648	SOD2
760	CA2	2100	ESR2	5027	P2RX7	6714	SRC
836	CASP3	2638	GC	5159	PDGFRB	6774	STAT3
729230	CCR2	3290	HSD11B1	5241	PGR	7046	TGFBR1
1080	CFTR	3291	HSD11B2	5468	PPARG	7099	TLR4
1268	CNR1	3480	IGF1R	5566	PRKACA	7124	TNF
1269	CNR2	5595	MAPK3	5734	PTGER4	7421	VDR
1513	CTSK	4314	MMP3	2185	PTK2B	7535	ZAP70
1586	CYP17A1	4317	MMP8	6256	RXRA

3.2 Functional enrichment analysis for potential targets in β-ecdysone treating osteoporosis

To understand biological functions of potential targets in β-ecdysone treating osteoporosis, GO and KEGG pathway enrichment analyses were carried out. Results showed that these targets were significantly enriched in 1,125 GO BP terms (such as steroid metabolic process and steroid biosynthetic process), 69 GO MF terms (such as steroid binding and steroid hormone receptor activity), 13 CC terms (such as membrane region and RNA polymerase II transcription factor complex), and 101 KEGG pathways (such as AGE-RAGE, prolactin, and HIF-1 signaling pathways). The representative enriched GO terms and KEGG pathways are shown in Figure 1.

Figure 1

The representative GO terms or KEGG pathways for potential targets of β-ecdysone treating osteoporosis.

3.3 PPI analysis and identification of hub genes

Using STRING database, a PPI network was constructed for potential targets in β-ecdysone treating osteoporosis. There were 44 protein nodes and 309 interactions in this PPI network (Figure 2a). The association between targets and the top 15 GO BP terms and KEGG pathways is shown in Figure 2b.

Figure 2

PPI and target-pathway networks for potential targets of β-ecdysone treating osteoporosis. (a) The PPI network constructed by proteins encoded by potential targets of β-ecdysone treating osteoporosis. The darker color represents higher degree of nodes. (b) The association between potential targets, top 15 GO BP terms, and top 15 KEGG pathways. Circles represent targets, purple quadrates represent KEGG pathways, and pink quadrates represent GO BP terms.

Moreover, the top 10 hub proteins with higher degrees in the PPI network were identified, including mitogen-activated protein kinase 3 (MAPK3), signal transducer and activator of transcription 3 (STAT3), toll-like receptor 4 (TLR4), caspase 3 (CASP3), albumin (ALB), tumor necrosis factor (TNF), peroxisome proliferator activated receptor gamma (PPARG), estrogen receptor 1 (ESR1), nuclear receptor subfamily 3 group C member 1 (NR3C1), and SRC proto-oncogene, non-receptor tyrosine kinase (SRC). The subnetwork constructed for these 10 hub proteins is displayed in Figure 3a. We further analyzed the association of these hub proteins with the top 15 GO BP terms (Figure 3b) and KEGG pathways (Figure 3c). Among them, TNF was found to be involved in more GO BP terms, such as steroid metabolic process, response to steroid hormone, and hormone biosynthetic process; ESR1 was also implicated in GO BP terms, such as response to steroid hormone and steroid metabolic process. Moreover, MAPK3 was associated with more KEGG pathways, such as osteoclast differentiation, HIF-1 signaling pathway, and estrogen signaling pathway; TLR4 was implicated in HIF-1 signaling pathway and lipid and atherosclerosis; and ESR1 was also involved in estrogen signaling pathway (Figure 3c).

Figure 3

Top 10 hub targets and their enrichment analysis. (a) The subnetwork constructed by the top 10 hub proteins with higher degrees in the PPI network. (b) The association of hub proteins with the top 15 GO BP terms. (c) The association of hub proteins with the top 15 KEGG pathways.

3.4 Molecular docking of β-ecdysone and hub proteins

To further investigate whether the hub proteins were the targets of β-ecdysone for osteoporosis, molecular docking analysis was conducted. The chemical structure of β-ecdysone is shown in Table 2. The results of molecular docking analysis showed that β-ecdysone could bind the active pocket of these hub proteins. Molecular docking results of the action mode of β-ecdysone with 10 hub proteins were exhibited in Figure 4. Moreover, we found that the binding energy of β-ecdysone and hub proteins was all less than −5.0 kcal/mol. Based on the binding energy value, β-ecdysone had the strongest binding force with TLR4, followed by TNF and ESR1 (Table 3).

Table 2

The chemical structure of β-ecdysone

Synonyms	Cas	Molecular formula	2D structure
β-Ecdysone	C27H44O7	5289-74-7

Figure 4

Molecular docking of β-ecdysone with top 10 hub osteoporosis-related proteins.

Table 3

Molecular docking results

Chem	Gene	PDB	Best affinity
β-Ecdysone	TLR4	2z65	−8.7
β-Ecdysone	TNF	2az5	−7.6
β-Ecdysone	ESR1	1hcq	−7.5
β-Ecdysone	CASP3	1gfw	−7.2
β-Ecdysone	NR3C1	1nhz	−6.9
β-Ecdysone	SRC	4hxj	−6.2
β-Ecdysone	STAT3	6njs	−5.7
β-Ecdysone	ALB	1bj5	−5.2
β-Ecdysone	MAPK3	2zoq	−5
β-Ecdysone	PPARG	1fm9	−5

4 Discussion

With an increased elderly population in China, osteoporosis has become a major public issue. Analysis of drug targets helps to develop potential therapeutic strategies for osteoporosis. Herein, we conducted network pharmacology and molecular docking to explore the target genes of β-ecdysone treating osteoporosis and analyzed their regulatory mechanisms. Results showed that 47 osteoporosis-related genes targeted by β-ecdysone were identified. By further PPI network analysis, the top 10 hub proteins were identified: TNF, ALB, SRC, STAT3, MAPK3, ESR1, PPARG, CASP3, TLR4, and NR3C1. Molecular docking analysis confirmed that β-ecdysone could bind the active pocket of these hub proteins, especially with TLR4, TNF, and ESR1. These targets may mediate the osteoprotective role of β-ecdysone in osteoporosis.

TLR4, one member of the TLR family, is revealed as an up-regulatory factor to aggravate osteoporosis [29,30]. Deletion of TLR4 has been reported to improve streptozotocin-induced diabetic osteoporosis in mice, suggesting that TLR4 may serve as potential therapeutic target for diabetic osteoporosis [31]. Inhibition of TLR4-mediated signaling pathways, such as TLR4/MyD88/NF-κB pathway, is recognized as a key mechanism mediating the preventive effect of AEG-1 deletion on osteoporosis in ovariectomized rats [32]. Vijayan et al. also demonstrated that methionine prevented bone loss during osteoporosis through downregulation of TLR-4/MyD88/NF-κB signaling pathway [33]. Molecular docking presented that β-ecdysone has a best binding activity with TLR4 (affinity = −8.7 kcal/mol), indicating that TLR4 is a key target for β-ecdysone treating osteoporosis. Moreover, our results showed that TLR4 was implicated in HIF-1 signaling pathway. In osteoblasts, activation of the HIF signaling pathway promotes the coupling process of bone formation and angiogenesis and plays a key role in preventing ovariectomy induced bone loss [34]. Ou et al. indicated that HIF signaling pathway might be one of the key mechanisms of Rehmanniae radix preparata to prevent osteoporosis [35]. Notably, HIF-1 signaling pathway plays a key role in spinal cord injury via regulating multiple targets, including TLR4 [36], implying the association between HIF-1 pathway and TLR4. Given the key roles of TLR4 and HIF-1 signaling pathway in osteoporosis, we propose that β-ecdysone may protect against osteoporosis via acting on TLR4 and HIF-1 signaling pathway.

TNF-α is a crucial cytokine that is implicated in autoimmune diseases [37]. Accumulating evidence has revealed that TNF-α can inhibit bone formation, which plays a critical role in estrogen deficiency-induced osteoporosis [38,39]. It is also reported that TNF-α can suppress osteogenic differentiation of bone marrow mesenchymal stem cells [40]. The reduction in the MSCs differentiation into osteoblasts by TNF-α can impair bone formation and thus lead to the pathogenesis of postmenopausal osteoporosis [41]. These data confirm the potential role of TNF-α in osteoporosis. Molecular docking showed that β-ecdysone has good binding affinity with TNF-α (−7.6 kcal/mol), implying the mitigative effect of β-ecdysone on osteoporosis by targeting TNF-α. Moreover, our results showed that TNF was involved in GO BP terms, such as steroid metabolic process, response to steroid hormone, and hormone biosynthetic process, which were associated with osteoporosis development [42,43]. Taken together, we speculate that β-ecdysone may prevent osteoporosis via targeting TNF to affect the steroid biosynthetic and metabolic processes.

ESR1 is one of the estrogen receptors and plays a significant role in bone metabolism [44]. It is reported that a phenotype of TNF-α regulation and spontaneous osteoporosis is observed in ESR1-deficient mice, implying that ESR1 may be a potential target for treating postmenopausal osteoporosis [45]. By molecular docking, we uncovered the binding correlation between β-ecdysone and ESR1 (affinity = −7.6 kcal/mol), demonstrating the targeting relationship of β-ecdysone on ESR1 in osteoporosis treatment. A recent study also showed that Bu-Yang herbs could alleviate postmenopausal osteoporosis through targeting ESR1 and estrogen signaling pathway [46]. Our results also revealed that ESR1 was enriched in estrogen signaling pathway. The estrogen signaling pathway plays a crucial role in regulating the differentiation, proliferation, and apoptosis of osteoblasts and osteoclasts [47]. Also, ESR1 and estrogen signaling pathway can be activated by natural products and has been considered key mechanisms of estrogen replacement therapy in the treatment of postmenopausal osteoporosis [48,49]. β-Ecdysone has similar structure with estrogen; therefore, it is considered to have steroid hormone-like effects including stimulating protein synthesis, promoting carbohydrate and lipid metabolism, and immunologic modulation effects [50]. Collectively, it can be speculated that β-ecdysone may exert anti-osteoporosis effect through regulating ESR1 and estrogen signaling pathway.

In conclusion, our results reveal that β-ecdysone may prevent osteoporosis development via regulating multiple targets, such as TLR4, TNF, and ESR1, and their corresponding pathways. These findings provide a theoretical basis for osteoporosis treatment. However, we focused on discussing the targeting of β-ecdysone on TLR4, TNF, and ESR1 in this article. Other hub targets binding to β-ecdysone are also needed to be explored and further investigated in vitro and in vivo. In the follow-up study, we plan to design pharmacological experiments to conduct in-depth investigation on the molecular mechanisms of β-ecdysone treating osteoporosis.

Acknowledgements

Not applicable.

Funding information: This work was supported by the Bureau of Science and Technology of Zhoushan (grant numbers 2022C31022 and 2021C31099).
Author contributions: B.Z. – conceptualization, writing – original draft. X.W. – formal analysis. W.L. – formal analysis. X.H. – writing-review & editing.
Conflict of interest: Authors state no conflict of interest.
Ethical approval: The conducted research is not related to either human or animal use.
Data availability statement: The datasets generated during the current study are available from the corresponding author on reasonable request.

References

[1] Lorentzon M, Johansson H, Harvey NC, Liu E, Vandenput L, McCloskey EV, et al. Osteoporosis and fractures in women: The burden of disease. Climacteric. 2022;25:4–10.10.1080/13697137.2021.1951206Search in Google Scholar PubMed

[2] Zhu X, Bai W, Zheng H. Twelve years of gwas discoveries for osteoporosis and related traits: Advances, challenges and applications. Bone Res. 2021;9:1–19.10.1038/s41413-021-00143-3Search in Google Scholar PubMed PubMed Central

[3] Nguyen BN, Hoshino H, Togawa D, Matsuyama Y. Cortical thickness index of the proximal femur: A radiographic parameter for preliminary assessment of bone mineral density and osteoporosis status in the age 50 years and over population. ClOrthopedic Surg. 2018;10:149–56.10.4055/cios.2018.10.2.149Search in Google Scholar PubMed PubMed Central

[4] Barnsley J, Buckland G, Chan PE, Ong A, Ramos AS, Baxter M, et al. Pathophysiology and treatment of osteoporosis: Challenges for clinical practice in older people. Aging Clin Exp Res. 2021;33:759–73.10.1007/s40520-021-01817-ySearch in Google Scholar PubMed PubMed Central

[5] Reid IR. Short-term and long-term effects of osteoporosis therapies. Nat Rev Endocrinol. 2015;11:418–28.10.1038/nrendo.2015.71Search in Google Scholar PubMed

[6] Coughlan T, Dockery F. Osteoporosis and fracture risk in older people. Clin Med. 2014;14:187–91.10.7861/clinmedicine.14-2-187Search in Google Scholar PubMed PubMed Central

[7] Wang L, Yu W, Yin X, Cui L, Tang S, Jiang N, et al. Prevalence of osteoporosis and fracture in china: The china osteoporosis prevalence study. JAMA Netw Open. 2021;4:e2121106.10.1001/jamanetworkopen.2021.21106Search in Google Scholar PubMed PubMed Central

[8] Bellavia D, Caradonna F, Dimarco E, Costa V, Carina V, De Luca A, et al. Terpenoid treatment in osteoporosis: This is where we have come in research. Trends Endocrinol Metab. 2021;32:846–61.10.1016/j.tem.2021.07.011Search in Google Scholar PubMed

[9] Li H, Xiao Z, Quarles LD, Li W. Osteoporosis: Mechanism, molecular target and current status on drug development. Curr Med Chem. 2021;28:1489–507.10.2174/0929867327666200330142432Search in Google Scholar PubMed PubMed Central

[10] Boo KH, Lee D, Jeon GL, Ko SH, Cho SK, Kim JH, et al. Distribution and biosynthesis of 20-hydroxyecdysone in plants of achyranthes japonica nakai. Biosci, Biotechnol, Biochem. 2010;74:2226–31.10.1271/bbb.100410Search in Google Scholar PubMed

[11] Seidlova-Wuttke D, Christel D, Kapur P, Nguyen BT, Jarry H, Wuttke W. B-ecdysone has bone protective but no estrogenic effects in ovariectomized rats. Phytomedicine. 2010;17:884–9.10.1016/j.phymed.2010.03.021Search in Google Scholar PubMed

[12] Zhang R, Hu SJ, Li C, Zhang F, Gan HQ, Mei QB. Achyranthes bidentata root extract prevent ovx-induced osteoporosis in rats. J Ethnopharmacol. 2012;139:12–8.10.1016/j.jep.2011.05.034Search in Google Scholar PubMed

[13] Singh D, Chaudhuri PK. Chemistry and pharmacology of Tinospora cordifolia. Nat Prod Commun. 2017;12:299–308.10.1177/1934578X1701200240Search in Google Scholar

[14] Abiramasundari G, Sumalatha KR, Sreepriya M. Effects of Tinospora cordifolia (menispermaceae) on the proliferation, osteogenic differentiation and mineralization of osteoblast model systems in vitro. J Ethnopharmacol. 2012;141:474–80.10.1016/j.jep.2012.03.015Search in Google Scholar PubMed

[15] Dai W-W, Wang L-B, Jin G-Q, Wu H-J, Zhang J, Wang C-L, et al. Beta-ecdysone protects mouse osteoblasts from glucocorticoid-induced apoptosis in vitro. Planta Medica. 2017;83:888–94.10.1055/s-0043-107808Search in Google Scholar PubMed

[16] Chakraborty S, Basu S. Dual inhibition of bace1 and aβ aggregation by β-ecdysone: Application of a phytoecdysteroid scaffold in alzheimer’s disease therapeutics. Int J Biol Macromol. 2017;95:281–7.10.1016/j.ijbiomac.2016.11.061Search in Google Scholar PubMed

[17] Kapur P, Wuttke W, Jarry H, Seidlova-Wuttke D. Beneficial effects of β-ecdysone on the joint, epiphyseal cartilage tissue and trabecular bone in ovariectomized rats. Phytomedicine. 2010;17:350–5.10.1016/j.phymed.2010.01.005Search in Google Scholar PubMed

[18] Dai W, Jiang L, Lay YA, Chen H, Jin G, Zhang H, et al. Prevention of glucocorticoid induced bone changes with beta-ecdysone. Bone. 2015;74:48–57.10.1016/j.bone.2015.01.001Search in Google Scholar PubMed PubMed Central

[19] Han J, Guan J, Zhu X. B-ecdysone attenuates cartilage damage in a mouse model of collagenase-induced osteoarthritis via mediating foxo1/adamts-4/5 signaling axis. Histol Histopathol. 2021;36:785–94.Search in Google Scholar

[20] Singh P, Gupta A, Qayoom I, Singh S, Kumar A. Orthobiologics with phytobioactive cues: A paradigm in bone regeneration. Biomed Pharmacotherapy. 2020;130:110754.10.1016/j.biopha.2020.110754Search in Google Scholar PubMed

[21] Abiramasundari G, Gowda CM, Sreepriya M. Selective estrogen receptor modulator and prostimulatory effects of phytoestrogen β-ecdysone in Tinospora cordifolia on osteoblast cells. J Ayurveda Integr Med. 2018;9:161–8.10.1016/j.jaim.2017.04.003Search in Google Scholar PubMed PubMed Central

[22] Gao L, Cai G, Shi X. Beta-ecdysterone induces osteogenic differentiation in mouse mesenchymal stem cells and relieves osteoporosis. Biol Pharm Bull. 2008;31:2245–9.10.1248/bpb.31.2245Search in Google Scholar PubMed

[23] Poornima P, Kumar J, Zhao Q, Blunder M, Efferth T. Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature. Pharmacol Res. 2016;111:290–302.10.1016/j.phrs.2016.06.018Search in Google Scholar PubMed

[24] Li X, Lin B, Lin Z, Ma Y, Wang Q, Zheng Y, et al. Exploration in the mechanism of fucosterol for the treatment of non-small cell lung cancer based on network pharmacology and molecular docking. Sci Rep. 2021;11:4901.10.1038/s41598-021-84380-wSearch in Google Scholar PubMed PubMed Central

[25] Rappaport N, Nativ N, Stelzer G, Twik M, Guan-Golan Y, Stein TI, et al. Malacards: An integrated compendium for diseases and their annotation. Database: J Biol Databases Curation. 2013;2013:bat018.10.1093/database/bat018Search in Google Scholar PubMed PubMed Central

[26] Pichler K, Warner K, Magrane M. Spin: Submitting sequences determined at protein level to uniprot. Curr Protoc Bioinforma. 2018;62:e52.10.1002/cpbi.52Search in Google Scholar PubMed PubMed Central

[27] Nickel J, Gohlke BO, Erehman J, Banerjee P, Rong WW, Goede A, et al. Superpred: Update on drug classification and target prediction. Nucleic Acids Res. 2014;42:W26–31.10.1093/nar/gku477Search in Google Scholar PubMed PubMed Central

[28] Trott O, Olson AJ. Autodock vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31:455–61.10.1002/jcc.21334Search in Google Scholar PubMed PubMed Central

[29] Alonso-Pérez A, Franco-Trepat E, Guillán-Fresco M, Jorge-Mora A, López V, Pino J, et al. Role of toll-like receptor 4 on osteoblast metabolism and function. Front Physiol. 2018;9:504.10.3389/fphys.2018.00504Search in Google Scholar PubMed PubMed Central

[30] Takeda K, Akira S, editors. TLR signaling pathways. Seminars in immunology. Amsterdam, NL: Elsevier; 2004.10.1016/j.smim.2003.10.003Search in Google Scholar PubMed

[31] Cao Y, Han X, Wang Z, Liu Y, Wang Y, Zhang R, et al. Tlr4 knockout ameliorates streptozotocin-induced osteoporosis in a mouse model of diabetes. Biochem Biophys Res Commun. 2021;546:185–91.10.1016/j.bbrc.2021.01.102Search in Google Scholar PubMed

[32] Zhang Y, Zhao Q. Aeg-1 deletion promotes cartilage repair and modulates bone remodeling-related cytokines via tlr4/myd88/nf-κb inhibition in ovariectomized rats with osteoporosis. Ann Transl Med. 2020;8:20–5842.10.21037/atm-20-5842Search in Google Scholar PubMed PubMed Central

[33] Vijayan V, Khandelwal M, Manglani K, Gupta S, Surolia A. Methionine down-regulates tlr 4/myd 88/nf-κ b signalling in osteoclast precursors to reduce bone loss during osteoporosis. Br J Pharmacol. 2014;171:107–21.10.1111/bph.12434Search in Google Scholar PubMed PubMed Central

[34] Zhao Q, Shen X, Zhang W, Zhu G, Qi J, Deng L. Mice with increased angiogenesis and osteogenesis due to conditional activation of hif pathway in osteoblasts are protected from ovariectomy induced bone loss. Bone. 2012;50:763–70.10.1016/j.bone.2011.12.003Search in Google Scholar PubMed

[35] Ou L, Kang W, Liang Z, Gao F, Dong T, Wei P, et al. Investigation of anti-osteoporosis mechanisms of rehmanniae radix preparata based on network pharmacology and experimental verification. J Orthopaedic Surg Res. 2021;16:599.10.1186/s13018-021-02751-5Search in Google Scholar PubMed PubMed Central

[36] Gong C, Liu L, Shen Y. Biomarkers mining for spinal cord injury based on integrated multi-transcriptome expression profile data. J Orthopaedic Surg Res. 2021;16:1–8.10.1186/s13018-021-02392-8Search in Google Scholar PubMed PubMed Central

[37] Jang D-I, Lee A-H, Shin H-Y, Song H-R, Park J-H, Kang T-B, et al. The role of tumor necrosis factor alpha (tnf-α) in autoimmune disease and current tnf-α inhibitors in therapeutics. Int J Mol Sci. 2021;22:2719.10.3390/ijms22052719Search in Google Scholar PubMed PubMed Central

[38] Redlich K, Smolen JS. Inflammatory bone loss: Pathogenesis and therapeutic intervention. Nat Rev Drug Discovery. 2012;11:234–50.10.1038/nrd3669Search in Google Scholar PubMed

[39] Walsh MC, Takegahara N, Kim H, Choi Y. Updating osteoimmunology: Regulation of bone cells by innate and adaptive immunity. Nat Rev Rheumatol. 2018;14:146–56.10.1038/nrrheum.2017.213Search in Google Scholar PubMed PubMed Central

[40] Yu L, Xu Y, Qu H, Yu Y, Li W, Zhao Y, et al. Decrease of mir-31 induced by tnf-α inhibitor activates satb2/runx2 pathway and promotes osteogenic differentiation in ethanol-induced osteonecrosis. J Cell Physiol. 2019;234:4314–26.10.1002/jcp.27210Search in Google Scholar PubMed

[41] Wang Y-W, Xu D-P, Liu Y, Zhang R, Lu L. The effect of tumor necrosis factor-α at different concentrations on osteogenetic differentiation of bone marrow mesenchymal stem cells. J Craniofacial Surg. 2015;26:2081–5.10.1097/SCS.0000000000001971Search in Google Scholar PubMed

[42] Xia T, Dong X, Jiang Y, Lin L, Dong Z, Shen Y, et al. Metabolomics profiling reveals rehmanniae radix preparata extract protects against glucocorticoid-induced osteoporosis mainly via intervening steroid hormone biosynthesis. Molecules. 2019;24:253.10.3390/molecules24020253Search in Google Scholar PubMed PubMed Central

[43] Liu Z-W, Luo Z-H, Meng Q-Q, Zhong P-C, Hu Y-J, Shen X-L. Network pharmacology-based investigation on the mechanisms of action of morinda officinalis how. In the treatment of osteoporosis. Comput Biol Med. 2020;127:104074.10.1016/j.compbiomed.2020.104074Search in Google Scholar PubMed

[44] Krela-Kaźmierczak I, Skrzypczak-Zielińska M, Kaczmarek-Ryś M, Michalak M, Szymczak-Tomczak A, Hryhorowicz ST, et al. Esr1 gene variants are predictive of osteoporosis in female patients with crohn’s disease. J Clin Med. 2019;8:1306.10.3390/jcm8091306Search in Google Scholar PubMed PubMed Central

[45] Raehtz S, Bierhalter H, Schoenherr D, Parameswaran N, McCabe LR. Estrogen deficiency exacerbates type 1 diabetes–induced bone tnf-α expression and osteoporosis in female mice. Endocrinology. 2017;158:2086–101.10.1210/en.2016-1821Search in Google Scholar PubMed PubMed Central

[46] Xia H, Liu J, Yang W, Liu M, Luo Y, Yang Z, et al. Integrated strategy of network pharmacological prediction and experimental validation elucidate possible mechanism of bu-yang herbs in treating postmenopausal osteoporosis via esr1. Front Pharmacol. 2021;12:952.10.3389/fphar.2021.654714Search in Google Scholar PubMed PubMed Central

[47] Kim RY, Yang HJ, Song YM, Kim IS, Hwang SJ. Estrogen modulates bone morphogenetic protein-induced sclerostin expression through the wnt signaling pathway. Tissue Eng Part A. 2015;21:2076–88.10.1089/ten.tea.2014.0585Search in Google Scholar

[48] Chiavarini M, Naldini G, Fabiani R. The role of diet in osteoporotic fracture healing: A systematic review. Curr Osteoporos Rep. 2020;18:138–47.10.1007/s11914-020-00573-8Search in Google Scholar PubMed

[49] Zhou L, Poon CC, Wong K-Y, Cao S, Dong X, Zhang Y, et al. Icariin ameliorates estrogen-deficiency induced bone loss by enhancing igf-i signaling via its crosstalk with non-genomic erα signaling. Phytomedicine. 2021;82:153413.10.1016/j.phymed.2020.153413Search in Google Scholar PubMed

[50] Dai W, Zhang H, Zhong ZA, Jiang L, Chen H, Lay YA, et al. B-ecdysone augments peak bone mass in mice of both sexes. Clin Orthop Relat Res. 2015;473:2495–504.10.1007/s11999-015-4246-5Search in Google Scholar PubMed PubMed Central

Received: 2022-10-25

Revised: 2022-11-29

Accepted: 2022-11-30

Published Online: 2022-12-31

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/chem-2022-0262

Keywords for this article

β-ecdysone; osteoporosis; network pharmacology; molecular docking; functional enrichment analysis

Creative Commons

BY 4.0