INTRODUCTION
Osteoarthritis (OA) is a chronic illness that is characterized by cartilage deterioration, which causes joint pain and stiffness. Many risk factors for the condition have been identified, including age, joint trauma, obesity, and hereditary factors (Hunter et al., 2014). According to recent studies, more than one-third of the elderly population has been affected by OA, and the chance of prevalence is expected to rise because of the rising incidence of obesity and the aging global population (Dillon et al., 2006). OA is caused by an imbalance in the creation and degradation of the cartilage matrix, which results in structural alterations in the joint (Wallace et al., 2017). Senescence and inflammation of chondrocytes, the primary cells of the cartilage matrix, are key factors in this imbalance. Targeting chondrocytes has been considered an essential approach to treat OA.
Trianthema portulacastrum L. is a medicinal plant in the “Aizoaceae” family that is also known as “desert horse purslane,” “black pigweed,” and “giant pigweed.” It has been used historically in many regions of the world to cure a number of diseases. The plant has been found to have a variety of pharmacological effects such as antimicrobial, anti-inflammatory, antidiabetic, anticancer, and wound-healing activities. The plant extracts have been utilized in the treatment of skin conditions, liver issues, and fever (Uttam et al., 2020). The roots of T. portulacastrum have been shown to contain potent antidiabetic effects because of the presence of different bioactive chemicals. The plant also contains various secondary metabolites, such as alkaloids, flavonoids, and terpenoids, all of which have been found to have therapeutic qualities (Kumar et al., 2005; Gaddeyya and Kumar, 2016). Due to its numerous health benefits, T. portulacastrum is a valuable medicinal plant that has significant promise in the manufacturing of novel drugs and therapies for several diseases.
Network pharmacology (NP) is a rapidly growing field that integrates the principles of systems biology, network analysis, and pharmacology to study complex diseases and drug action (Wu and Wu, 2015). This innovative research approach utilizes sophisticated computational tools and large-scale data analysis to obtain insights into drug action molecular processes and find prospective therapeutic targets (Boran and Iyengar, 2010; Noor et al., 2022). NP has several advantages over traditional drug discovery methods, including the ability to predict off-target effects, identify new drug combinations, and prioritize drug targets based on their relevance to disease pathogenesis (Agamah et al., 2020).
The innovative research technique of NP has recently emerged as a highly effective technique for identifying potential mechanisms underlying specific diseases through the analysis of databases and target predictions (Zhang et al., 2013). Likewise, molecular docking has become a popular in silico method for identifying the most effective constituents and proteins among vast collections of phytochemicals and proteins (Bhardwaj et al., 2021). Molecular dynamics (MD) simulation approaches were used for the validation, and the aim was to understand the behavior of the protein–ligand complex in the real system. The goal of this research is to discover the mechanism by which T. portulacastrum combats OA. To achieve this aim, we employed an NP approach to anticipate the core targets and pathways of T. portulacastrum. Subsequently, molecular docking was used to confirm the interaction between the main compounds and core targets of the important pathways. Lastly, in vivo and in vitro experiments were performed to validate the expected outcomes from NP.
MATERIALS AND METHODS
Prediction and screening of active constituents of T. portulacastrum
The active compound of T. portulacastrum was investigated using various literature and publicly available databases. To collect T. portulacastrum-related compounds, phytochemical databases such as KNApSAcK Core System (http://www.knapsackfamily.com/knapsack_core) (Afendi et al., 2012) and IMPPAT (https://cb.imsc.res.in/imppat/) (Mohanraj et al., 2018) were utilized. The databases were searched using the keyword “Trianthema portulacastrum.” PubMed (https://pubmed.ncbi.nlm.nih.gov/) (White, 2020) and Google Scholar (https://scholar.google.com/) (Vine, 2006) were also used for literature mining. All of the identified compounds were filtered for OB and DL, and only those meeting the precise standards of DL ≥ 0.18 and OB ≥ 30% were chosen for further investigation. The proportion of an orally taken medicine that enters the bloodstream and is accessible for utilization by the body is referred to as OB (Sietsema, 1989). DL, on the other hand, refers to the set of physicochemical properties that make a molecule suitable for drug development (Ursu et al., 2011). SwissADME (http://www.swissadme.ch/index.php) (Daina et al., 2017) and Molsoft L.L.C. (https://www.molsoft.com/) (Molsoft, 2018) were utilized to calculate OB and DL, respectively. PubChem (https://pubchem.ncbi.nlm.nih.gov/) (Kim et al., 2016) and ChemSpider (http://www.chemspider.com/) (Pence and Williams, 2010) were utilized to collect information on chemicals such as CID number, Canonical SMILES, and molecular weight of the predicted compounds. Finally, the two-dimensional (2D) structures of the active constituents were retrieved through Molinspiration Cheminformatics (https://www.molinspiration.com/) (Molinspiration, 2011).
Active constituent-related and disease-related target prediction
Target gene prediction is an important step in comprehending the molecular interactions of therapeutic plants in treating various diseases and disorders. To determine the target genes of the active constituents, the SMILES was used, and the Swiss Target Prediction (http://www.swisstargetprediction.ch/) (Gfeller et al., 2014) and STITCH (http://stitch.embl.de/) (Kuhn et al., 2007) databases were consulted. For the STITCH database, the name of each compound was entered, and the search was limited to “Homo sapiens.” Meanwhile, for the Swiss Target Prediction, the SMILES of the compounds was submitted for analysis. A reverse pharmacophore combination approach based on structural similarity was used to determine target genes. This methodology ensured the accurate identification of the target genes of the compounds, providing vital insight into the molecular processes of medicinal plants.
Prediction of disease-related targets was done through the Gene Expression Omnibus (GEO)-NCBI (https://www.ncbi.nlm.nih.gov/geo/) database (Barrett et al., 2012). Two microarray datasets (GSE55235 and GSE55457) were selected to identify differentially expressed genes (DEGs) in OA. Each dataset had 20 tissue samples, 10 of which were normal and 10 were affected samples. The R language Limma v.3.26.8 package was used to normalize the datasets, reducing redundant data and minimizing the errors related to data alteration (Smyth, 2005). DEGs were identified based on an adjusted P-value <0.05, logFC ≤−1 for downregulated genes, and logFC ≥1 for upregulated genes, which were defined as OA-specific genes. Volcano plots were created to visualize the major upregulated and downregulated genes in the normal versus OA comparison. The DEGs obtained through this analysis were then utilized for further research.
To find the genes shared by T. portulacastrum and OA targets, nonredundant compound-related targets and significant DEGs related to OA were intersected, and a Venn diagram was created. This allowed for a clear visualization of the overlapping genes between T. portulacastrum and OA targets. This approach provided comprehensive insight into the possible therapeutic effects of T. portulacastrum on OA.
Protein–protein interaction and network construction
To explore the protein–protein interaction (PPI) network of the overlapped genes between T. portulacastrum and OA targets, the STRING database (https://string-db.org/) (von Mering et al., 2003) was utilized, and the reference organism selected was “Homo sapiens.” This database contains experimental and anticipated association information based on techniques such as systematic, shared selection signals among genomes and automated text mining from scientific literature. In this study, interactions having a combined score of ≥0.4 were selected for further analysis.
To acquire a better understanding of the multiscale action mechanisms of T. portulacastrum in curing OA, two kinds of networks were generated using Cytoscape 3.9.1 (Shannon et al., 2003). The first network was a compound–target network, while the second was a compound–target–pathway network. The nodes in the network illustrated the compound, targets, and pathways; on the other hand, the solid lines depicted the interactions between those nodes. The network centrality algorithm degree, which is a topological measure, was used to determine the importance of each network component/target/pathway.
Gene Ontology enrichment and KEGG pathway analysis
To comprehend a deeper and best understanding of the functional roles of the overlapped target genes between T. portulacastrum and OA, the DAVID database (https://david.ncifcrf.gov/) (Dennis et al., 2003), the online functional annotation server, was employed. This tool was utilized to perform Gene Ontology (GO) and KEGG pathway analyses. The DAVID database predicted the functions of overlapping target genes at three levels: molecular function, biological process, and cellular component, as well as KEGG pathways. Based on the P-value, a bubble plot was generated in R utilizing the ggplot2 package (Wickham et al., 2016). GO keywords and KEGG pathways having P-values <0.05 were deemed statistically significant.
Analysis of molecular docking
To validate the core target, a molecular docking technique was employed in this study, which has become the most appropriate technique in the drug discovery toolbox. This method identifies the connections that maintain ligands bound to their respective proteins. To collect crystalline structures of the core target Jun proto-oncogene (JUN) (PDB ID: 1JUN), the PDB database (https://www.rcsb.org/) (Kouranov et al., 2006), which serves as a central archive for protein 3D structure knowledge, was used. The binding pockets of core protein were predicted using the CASTp 3.0 tool (http://sts.bioe.uic.edu/castp/index.html?201l) (Tian et al., 2018), and the PyRx program was utilized to perform docking between core protein and key active constituents (Dallakyan and Olson, 2015). Only docked postures with the lowest root mean square deviation (RMSD) and the highest binding affinity were chosen. The major assessment parameter for screening potential compounds and their suspected target was the docking score between protein and constituents. Furthermore, the interactions between important active chemicals and the anticipated protein/target were visualized using Discovery Studio (Accelrys Corporate Headquarters, 2008).
Analysis of MD simulations
MD simulations offer significant advantages in exploring bioactive compounds for OA treatment by providing detailed insights into the stability and behavior of protein–ligand complexes. The MD simulations of the docking complexes were run for 100 ns in order to validate the system’s structural stability. The simulations were performed utilizing the AMBER22 simulation package (Lobanov et al., 2008). The evaluation was completed by following the simulation approach that was described by Lobanov et al. (2008). To put it briefly, AMBER’s antechamber module (Shaikh et al., 2022) was used for pre-processing the systems, and the AMBER FF19SB force field (Brogi et al., 2020) was utilized to specify the parameters. The appropriate amounts of counterions were supplied to the systems to neutralize them once they had been solvated into the TIP3P water box. Afterwards, three types of energy minimization were applied to the systems such as hydrogen atom energy minimization, energy minimization of water box, and non-heavy atom energy minimization. The systems were then progressively heated to 300 K while being kept at that temperature using Langevin dynamics (Kräutler et al., 2001). In order to obtain constraints on the hydrogen bonds, we employed the SHAKE algorithm (Daoui et al., 2022). To equalize the pressure after the systems had been brought to equilibrium for 100 ps, The NPT ensemble was used (Samad et al., 2023). Moreover, a production run of the simulations was conducted on a 2-fs timescale for 100 ns. The simulated trajectories had been studied via multiple structural analyses using AMBER’s CPPTRAJ module (Samad et al., 2023). The trajectories were visually assessed using version 1.9.3 of the Visual Molecular Dynamics tool (Humphrey et al., 1996). The MMPBSA.py module of the AMBER simulation package was utilized further for estimating binding free energies. In total, 5000 frames were picked from simulation trajectories and used in molecular mechanics/generalized Born surface area (MMGBSA) and molecular mechanics/Poisson–Boltzmann surface area (MMPBSA) calculations.
RESULTS
Active compounds of T. portulacastrum
To understand the molecular processes involved in OA treatment by T. portulacastrum, it is essential to analyze the pharmacokinetic characteristics of its compounds. In this regard, the DL and OB of 62 active compounds of T. portulacastrum were examined based on data retrieved from various databases and literature (Supplementary File 1). Out of these 62 compounds, 7 compounds (ascorbic acid, beta-sitosterol, ecdysterone, nicotinic acid, protocatechuic acid, quercetin, and stigmasterol) were selected for further investigation as they have the required standard of OB ≥30% and DL ≥0.18 (as shown in Table 1). Analyzing these pharmacokinetic properties provides insights into the effectiveness of the compounds in treating OA and their potential for further development of drugs.
Information about T. portulacastrum’s putative active substances.
| Phytochemical | DL | OB | CID | MW | 2D structure |
|---|---|---|---|---|---|
| Quercetin | 0.52 | 0.55 | 5280343 | 302.23 |
|
| Ascorbic acid | 0.74 | 0.56 | 54670067 | 176.12 |
|
| Nicotinic acid | 0.3 | 0.85 | 938 | 123.11 |
|
| Ecdysterone | 1.37 | 0.55 | 5459840 | 480.6 |
|
| Beta-sitosterol | 0.78 | 0.55 | 222284 | 414.7 |
|
| Stigmasterol | 0.62 | 0.55 | 5280794 | 412.7 |
|
| Protocatechuic acid | 0.23 | 0.56 | 72 | 154.12 |
|
Abbreviation: MW, molecular weight.
Collectively, 444 probable target genes for the 7 active components were identified using the STITCH and Swiss Target Prediction databases (Supplementary File 2). After identifying the fascinating targets, 1694 genes that were associated with OA were collected from the 2 microarray datasets (GSE55235 and GSE55457). The GSE55235 had 1329 DEGs, comprising 534 upregulated and 795 downregulated genes (Supplementary File 3; Fig. 1a), whereas GSE55457 had 875 DEGs, including 754 upregulated and 121 downregulated genes (Supplementary File 4; Fig. 1b). The R script used to construct the volcano plot is provided in Supplementary File 5. The overlapping targets of OA and T. portulacastrum were then identified using a Venn diagram, and 70 putative anti-OA genes discovered in T. portulacastrum were chosen as key targets (Supplementary File 6; Fig. 1c).
Construction of the compound–target network
The T. portulacastrum has been found to contain 7 potential active compounds and 444 corresponding targets. To further analyze these compounds, a network was created using 444 targets, which included 7 plant-related compounds (Fig. 2). The resulting network contained 451 nodes and 809 edges, providing evidence that T. portulacastrum may have a synergistic impact as an anti-OA drug on multiple targets. To determine the significance of each compound in the network, the degree of each compound was calculated using the CytoHubba plugin of Cytoscape. Beta-sitosterol, stigmasterol, nicotinic acid, ecdysterone, quercetin, protocatechuic acid, and ascorbic acid were found to have degrees of 135, 131, 126, 123, 117, 114, and 63, respectively. These results indicate that these compounds could have important roles in the anti-OA activity of T. portulacastrum.
Protein–protein interaction
The 70 overlapping targets were added to the STRING database in order to build a PPI network. The generated PPI network represented the interaction of multiple targets during illness progression through nodes and their associated interactions. This network was further analyzed using a network CytoHubba plugin of Cytoscape, which revealed that JUN (31), EGFR (30), PTGS2 (25), HSP90AA1 (24), MAPK14 (23), ESR1 (23), MMP9 (21), ICAM1 (19), PTPN11 (18), and APOE (17) showed the greatest degree of connection (Fig. 3).
(a) Top 10 genes predicted by CytoHubba, (b) their bar plot of degree, and (c) co-expression.
The higher degree of connection among these targets indicates that they are highly interconnected, suggesting that all of these target genes might be potential targets for the anti-OA effects of T. portulacastrum. One gene named JUN was identified as the main anti-OA target of T. portulacastrum and processed further for molecular docking analysis.
GO and KEGG pathway analysis
The analysis of 70 common targets indicated their biological functions by pathways and enrichment analysis. Furthermore, the GO enrichment results illustrated that the target genes were primarily involved in response to drugs, xenobiotic stimuli, protein binding, ATP binding, and other related functions. On the other hand, the KEGG pathway analysis showed that the genes were considerably abundant in pathways related to cancer, Rap1 signaling pathways, lipid and atherosclerosis, and interleukin (IL)-17 signaling pathways (Supplementary File 7; Fig. 4). The R script used to construct the volcano plot is provided in Supplementary File 8.
Compound–target–pathway network construction
To fully comprehend T. portulacastrum’s multi-target impact in treating OA, two distinct networks, the “compound–target network” and the “target–pathway network,” were built using Cytoscape, version 3.9.1. The target–pathway network comprised 29 nodes along with 80 edges, whereas the compound–target network had 17 nodes and 20 edges (Fig. 5). To provide a more comprehensive picture, these 2 networks were combined to form a compound–target–pathway network with 37 nodes and 99 edges. This network provides a more comprehensive picture of T. portulacastrum’s multi-target impact in OA treatment.
T. portulacastrum-induced pathways. The orange nodes indicate hub genes, the green nodes indicate active chemicals, and the blue nodes are pathways connected to main targets. Abbreviations: EGFR, epidermal growth factor receptor; MAPK, mitogen-activated protein kinase; PD-L1, programmed cell death ligand 1; PD-1, programmed cell death 1; GnRH, Gonadotropin hormone-releasing hormone; MMP, matrix metallopeptidase; APOE, apolipoprotein E; IL, interleukin; TNF, tumor necrosis factor.
Molecular docking
In this study, seven active compounds of T. portulacastrum were subjected to molecular docking with the JUN target protein. The findings revealed that all seven compounds exhibited good binding affinity and a great degree of matching with the core JUN protein. Specifically, JUN demonstrated greater binding affinity with quercetin (−5.7 kcal/mol), stigmasterol (−5.5 kcal/mol), and ecdysterone (−5.1 kcal/mol). All compounds possessed strong bonding to the conserved ILE A:277 and ARG B:276 residues except ascorbic acid and nicotinic acid. These findings provide strong evidence that the active compounds of T. portulacastrum can stably bind to the JUN target protein, which in turn can serve as an effective treatment for OA. The results also suggest that further research can focus on identifying the binding pockets of these active ingredients with the core protein (Fig. 6; Table 2).
Mechanisms of interaction and binding modes of inhibitors of JUN protein. A 3D depiction into the binding mode of (a) quercetin, (b) stigmasterol, and (c) ecdysterone. A 2D interaction analysis of (d) quercetin, (e) stigmasterol, and (f) ecdysterone.
Binding energy and interactions between active chemicals and the two core proteins.
| Protein | Compound | Binding affinity (kcal/mol) | RMSD (Å) | Interacting residues |
|---|---|---|---|---|
| JUN | Quercetin | −5.7 | 1.839 | ILE A:277, ARG B:276, LEU B:280 |
| Stigmasterol | −5.5 | 1.244 | ILE A:277, ALA A:278, ARG B:276, ARG B:279, LEU B:280 | |
| Ecdysterone | −5.1 | 1.786 | ILE A:277, ALA A:278, ARG B:276, ARG B:279, LEU B:280 | |
| Beta-sitosterol | −4.7 | 1.549 | ILE A:277, ALA A:278, ARG B:276, ILE B:277, ILE B:280 | |
| Protocatechuic acid | −3.7 | 1.717 | CYS A:273, ILE A:277, CYS B:273, GLY B:274, GLY B:275, ARG B:276 | |
| Ascorbic acid | −3.5 | 1.426 | ARG B:279, THR B:286 | |
| Nicotinic acid | −3.1 | 0.528 | LEU A:280, LYS A:283, LYS B:288 |
Abbreviation: RMSD, root mean square deviation.
MD simulations
The dynamic behavior of macromolecules is fundamentally confirmed by MD simulation (Lobanov et al., 2008). The simulation study includes RMSD (Brogi et al., 2020), radius of gyration (RoG) (Shahab et al., 2022), and root mean square fluctuation (RMSF) (Shaikh et al., 2022). The foundation for all of these studies was the complexes’ carbon alpha atom. These investigations were carried out to find out whether the interactions continued during the simulation time and whether the ligand binding to the receptors was sustained. Stable receptor–ligand binding will guarantee that the ligand is presented to the JUN protein appropriately. Throughout the simulation, the RMSD plot of the systems remained consistent, indicating little to no structural alterations. The RMSD of the systems ranged from 2 to 3 Å, with the mean values for JUN_ecdysterone, JUN_quercetin, and JUN_stigmasterol being 2.10 Å, 2.15 Å, and 2.06 Å, respectively, and the highest values being 3.22 Å, 2.96 Å, and 3.23 Å, respectively (Fig. 7a). Second, the RMSF was measured to reveal data about the flexibility of the receptor residues in the presence of the ligand molecule (Fig. 7b). Almost all of the system residues were falling in the good stability range (<4.5 Å). For JUN_ecdysterone, JUN_quercetin, and JUN_stigmasterol, the RMSF of the systems revealed maximum values of 3.23 Å, 4.03 Å, and 4.42 Å; the mean values were 0.98 Å, 1.21 Å, and 0.99 Å; and the minimum values were 0.42 Å, 0.47 Å, and 0.42 Å, respectively. It was found that certain residues had a greater level of flexibility caused by the loop pressure on the system. Even so, the way that ligands attached to the receptors was not influenced by these variations. Using the RoG analysis, the compactness of the system was tested against time. The values for JUN_ecdysterone, JUN_quercetin, and JUN_stigmasterol were as follows: the highest values were 22.72 Å, 23.01 Å, and 23.00 Å; the mean values were 22.09 Å, 22.31 Å, and 22.42 Å; and the minimum values were 21.53 Å, 21.81 Å, and 21.65 Å, respectively (Fig. 7c). According to RMSD, each system was compact and was not significantly changing as the simulation came to wrap up. The findings of the beta factor analysis were as follows: The average values were 31.54 Å, 48.52 Å, and 31.41 Å; the lowest values were 4.71 Å, 5.99 Å, and 4.72 Å; and the highest values were 274.57 Å, 427.68 Å, and 516.39 Å for JUN_ecdysterone, JUN_quercetin, and JUN_stigmasterol, respectively (Fig. 7d). To learn more about the surface area of the JUN protein that interacts with the solvent molecules, solvent-accessible surface area (SASA) analysis had been conducted for the ligands. The mean values of the systems for JUN_stigmasterol, JUN_ecdysterone, and JUN_quercetin were 17,416.6 Å2, 16,965.3 Å2, and 17,436.4 Å2, respectively. According to Figure 8, the lowest values of SASA analysis for JUN_ecdysterone, JUN_quercetin, and JUN_stigmasterol were 14,920 Å2, 15,762.1 Å2, and 15,698.5 Å2, respectively, while the highest values were 18,406.5 Å2, 18,749.7 Å2, and 18,808 Å2, correspondingly. The RMSD figure (Fig. 7a) shows that there were no significant variances and that all of the systems were compact.
Binding free energy estimation
In order to validate the intermolecular interactions of the selected docked complexes, MMGBSA and MMPBSA binding free energies were estimated for the complexes using 5000 frames from the simulation trajectories. Both methods are modest in terms of using computational power and give findings that are comparable to experimental findings. Table 3 describes the intermolecular energies of the complexes. The van der Waals energy was found to play a significant role in stabilizing the compounds with the receptor enzyme. The net van der Waals energy of JUN_ecdysterone, JUN_quercetin, and JUN_ stigmasterol was −36.01 kcal/mol, −37.84 kcal/mol, and −40.21 kcal/mol, respectively. The electrostatic energy of the complexes was −14.20, −15.64, and −13.01 kcal/mol for JUN_ecdysterone, JUN_quercetin, and JUN_stigmasterol, respectively. All the complexes revealed robust binding energies and have nonfavorable solvation energy. In MMGBSA, the net binding energy score was −40.88, −43.26, and −43.55 kcal/mol for JUN_ecdysterone, JUN_quercetin, and JUN_stigmasterol, respectively. The MMPBSA binding energy of the complexes was −41.54 kcal/mol, −43.23 kcal/mol, and −43.59 kcal/mol for JUN_ecdysterone, JUN_quercetin, and JUN_stigmasterol, respectively.
Estimated MMGBSA and MMPBSA binding free energies of complexes.
| Parameter | JUN_ecdysterone | JUN_quercetin | JUN_stigmasterol |
|---|---|---|---|
| MMGBSA | |||
| VdW energy | −36.01 | −37.84 | −40.21 |
| Electrostatic energy | −14.20 | −15.64 | −13.01 |
| Gas phase energy | −50.21 | −53.48 | −53.22 |
| Solvation energy | 9.33 | 10.22 | 9.67 |
| Net energy | −40.88 | −43.26 | −43.55 |
| MMPBSA | |||
| VdW energy | −36.01 | −37.84 | −40.21 |
| Electrostatic energy | −14.20 | −15.64 | −13.01 |
| Gas phase energy | −50.21 | −53.48 | −53.22 |
| Solvation energy | 8.67 | 10.25 | 9.63 |
| Net energy | −41.54 | −43.23 | −43.59 |
Abbreviations: MMGBSA, molecular mechanics/generalized Born surface area; MMPBSA, molecular mechanics/Poisson–Boltzmann surface area; VdW, van der Waals.
DISCUSSION
OA is a degenerative joint condition in which cartilage degrades in the joints. It is the most frequent kind of arthritis and is connected with age and joint wear and tear (Musumeci et al., 2015). The specific origin of OA is unknown, although several risk factors such as obesity, joint injury or overuse, heredity, and hormone imbalances have been found (Bijlsma and Knahr, 2007). Symptoms of OA typically include joint pain, stiffness, and swelling, particularly in the knees, hips, and hands. As the disease progresses, joint mobility may become restricted, and the affected joint may become deformed. In extreme circumstances, joint replacement surgery may be required (Kolasinski et al., 2020). While there is no cure for OA, pain medication, lifestyle changes, and physical therapy such as exercise and weight loss exercise can help maintain joint function and quality of life.
T. portulacastrum, also known as black pigweed, is a therapeutic plant and used for centuries in traditional medicine for millennia to cure a variety of maladies such as diabetes, inflammation, fever, and gastrointestinal issues (Sukalingam et al., 2017). The plant contains a variety of phytochemicals, including alkaloids, flavonoids, and phenolic acids, that have been shown to exhibit a range of pharmacological activities such as antioxidant, anti-inflammatory, antimicrobial, and anticancer activities (Ahmed and Slima, 2020). The aqueous whole plant extract of T. portulacastrum has antinociceptive and anti-arthritic effects in rodents (Falade et al., 2019). Several studies have investigated the therapeutic potential of T. portulacastrum in various disease models, including cancer, diabetes, and obesity, with promising results.
The study investigated the active components and targets of T. portulacastrum using an NP approach. To achieve this, several databases, including Swiss Target Prediction and STITCH, were utilized to screen for potential targets of the plant’s active components. The DEGs related to OA were extracted from the publicly available datasets GSE55235 and GSE55457 from the GEO-NCBI database, and plant-related and disease-related DEGs were intersected to identify overlapping targets. A total of 70 possible target genes were discovered, and 10 hub genes were chosen using a degree threshold from the PPI network.
The GO functional enrichment study conducted in this investigation demonstrated that the hub genes were significantly associated with various GO keywords, including response to drugs, response to xenobiotic stimuli, protein binding, ATP binding, and more. In addition, the KEGG pathway analysis showed that the possible target genes were involved in metabolic pathways, pathways in tumor necrosis factor signaling, IL-17 signaling, programmed cell death ligand 1 (PD-L1) expression, and the programmed cell death 1 (PD-1) checkpoint pathway in cancer. Further analysis of the PPI and KEGG pathways showed that JUN was a significantly enriched gene, which was then submitted for molecular docking analysis to validate potential interaction with the active compounds of T. portulacastrum.
From the results of the KEGG pathway and network analyses, JUN was recognized as an essential target for OA treatment. This target was further evaluated for its potential anti-OA effectiveness by binding with seven active components of T. portulacastrum using molecular docking analysis. The findings revealed that the chemicals quercetin, stigmasterol, and ecdysterone were capable of stable interactions with the binding sites of JUN, indicating their potential to suppress the gene and treat OA.
The MD simulations elucidated the dynamic behavior of JUN protein–ligand complexes, crucial for understanding their stability and interactions (Sadaqat et al., 2023). The consistent RMSD plots indicated minimal structural alterations, ensuring stable receptor–ligand binding throughout the simulations. RMSF analysis revealed that most residues maintained good stability, with ligands not significantly influencing receptor attachment. RoG analysis confirmed the compactness of systems, and beta factor analysis highlighted the overall stability. SASA analysis provided insights into JUN interactions with solvent molecules. The calculated binding free energies using MMGBSA and MMPBSA affirmed strong interactions, with van der Waals forces playing a pivotal role. Electrostatic energies contributed to binding stability, yielding robust net binding energies for JUN_ecdysterone, JUN_quercetin, and JUN_stigmasterol. These findings validate the reliability of the selected docked complexes and provide comprehensive insights into the MD of JUN–ligand interactions, essential for drug design and therapeutic strategies.
An NP technique was employed in this study to understand the mechanism of action of active medicines, identify their related target genes, and link pathways for the treatment of OA. While the findings are encouraging, more experimental research is needed to validate and confirm the efficacy of T. portulacastrum and its active components in treating OA. Our research has various limitations. Initially, more experiments are needed to validate our results. Second, in order to improve the accuracy of the results of the NP study, a larger database of conventional medications and target genes is needed. Third, we were still not able to fully understand the exact therapeutic mechanism of T. portulacastrum, even after integrating the results of NP with molecular docking. Therefore, it took the integration of several disciplines to comprehend T. portulacastrum’s action mechanism in OA. Nonetheless, this investigation provides a valuable foundation for future research in the creation of novel and effective therapies for OA.
CONCLUSION
NP was employed to unravel the comprehensive mechanism of action of T. portulacastrum in treating OA. Traditional herbal medicine aims to enhance the overall well-being of the patient with minimal side effects by using herbal formulations. The results suggest that the compounds present in T. portulacastrum, including ascorbic acid, beta-sitosterol, ecdysterone, nicotinic acid, protocatechuic acid, quercetin, and stigmasterol, may have an important role in mediating the therapeutic impacts of this plant in OA. The MD simulations unveiled the structural stability and dynamic interactions of JUN protein–ligand complexes. The robust binding energies, as validated by MMGBSA and MMPBSA, underscore the potential therapeutic significance of JUN_ecdysterone, JUN_quercetin, and JUN_stigmasterol, providing a solid foundation for further exploration and design of targeted drugs for relevant biological pathways. The study demonstrates the vast potential of NP in drug discovery, which offers crucial information that might assist in the development of new OA therapies.