Integrated network pharmacology reveals the mechanism of action of Xianlinggubao prescription for inflammation in osteoarthritis

Abstract

Background

Osteoarthritis (OA), a leading cause of disability worldwide, is characterized by complex interactions between cartilage degradation and synovial inflammation. While NSAIDs are the primary treatment, their prolonged use exacerbates gastrointestinal risks and does not alter disease progression. Xianlinggubao (XLGB), an approved Chinese herbal remedy for osteoporosis, has demonstrated promising anti-osteoarthritic effects in preliminary studies. However, its multi-component mechanisms targeting OA-related inflammation require further clarification. This study integrates network pharmacology with experimental validation to investigate XLGB's anti-inflammatory mechanisms in OA.

Methods

Bioactive compounds of XLGB and their respective targets were sourced from the TCMSP, ETCM, SymMap, and ChEMBL databases. Targets linked to OA-related inflammation were identified through differential expression analysis and by querying OMIM, GeneCards, and PubMed Gene databases. Network pharmacology and bioinformatics approaches were employed to construct compound-target and protein–protein interaction (PPI) networks, enabling the identification of pivotal therapeutic targets. Functional enrichment of these targets was performed using the ClusterProfiler package in R. The binding affinity of compounds to anti-inflammatory OA targets was assessed through molecular docking, dynamics simulations, RT-PCR, and immunofluorescence assays.

Results

Fifty-five bioactive compounds corresponding to 475 XLGB targets and 125 genes involved in OA-related inflammation were identified. PPI network analysis revealed that XLGB may alleviate OA inflammation by modulating key genes, including COX-2, IL-1β, TNF, IL-6, and MMP-9. Molecular simulations indicated strong binding affinities between bioactive compounds in XLGB and these critical targets. Functional enrichment analysis suggested that XLGB's anti-inflammatory action in OA may involve regulation of pathways such as IL-17, TNF, and NF-κB. In vitro experiments further confirmed that XLGB mitigates OA inflammation by modulating these genes, proteins, and signaling pathways.

Conclusions

Through network pharmacology, this study elucidated the mechanisms of XLGB in OA inflammation, highlighting its modulation of IL-6, IL-1β, TNF-α, PTGS2, MMP-9, and the NF-κB pathway. These findings provide strong support for the clinical application of XLGB in managing OA-related inflammation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-025-04928-5.

Introduction

Osteoarthritis (OA) is a prevalent age-related degenerative joint disorder, characterized by progressive articular cartilage degradation, accompanied by subchondral bone sclerosis and pathological remodeling [1, 2]. However, the precise etiological mechanisms driving OA pathogenesis remain incompletely understood. Current clinical management predominantly involves pharmacological interventions, such as nonsteroidal anti-inflammatory drugs (NSAIDs) and opioid analgesics [3, 4], alongside surgical procedures like osteotomy, arthroplasty, and arthrodesis [5, 6]. These treatments, however, are frequently associated with adverse effects, including surgical complications and cardiovascular risks [4, 7, 8]. Given that OA is linked to a persistent inflammatory response within joint tissues, driven by various immune cells, further understanding of the interactions between pro-inflammatory cytokines and immune cells is critical to elucidating its pathogenesis. Inflammatory cytokines and chemokines, synovial responses, immune cell infiltration, and the activation of inflammatory pathways all contribute to OA progression.

Traditional Chinese medicine (TCM) has been widely employed in clinical practice for managing OA due to its minimal side effects, substantial efficacy, and cost-effectiveness [9]. Xianlinggubao Prescription (XLGB), composed of six herbs—Yinyanghuo (Epimedium brevicornu Maxim., 70%), Zhimu (Anemarrhena asphodeloides Bunge, 5%), Danshen (Salvia miltiorrhiza Bunge, 5%), Buguzhi (Cullen corylifolium (L.) Medik., 5%), Xuduan (Dipsacus asperoides C.Y.Cheng & T.M.Ai, 10%), and Dihuang (Rehmannia glutinosa (Gaertn.) DC., 5%)—has shown effectiveness in treating conditions such as osteoporosis, aseptic osteonecrosis, OA, and bone fractures. Clinical studies suggest that XLGB enhances superoxide dismutase activity, reduces malondialdehyde and nitric oxide (NO) levels, and suppresses the expression of sensitive biomarkers such as C-reactive protein, TNF-α, and IL-6 in patients with OA [10]. However, the active components and specific anti-inflammatory targets of XLGB in OA remain unclear.

Systems pharmacology approaches, incorporating systems biology, cheminformatics, bioinformatics, multidirectional network biology, and traditional pharmacology [11], have been employed to identify bioactive compounds in TCM formulations and predict their mechanisms of action. In this study, network pharmacology, combined with bioinformatics analysis, was utilized to predict XLGB's anti-inflammatory targets in OA, with subsequent validation of these targets to provide insights for future clinical applications (Fig. 1).

Fig. 1.

Schematic representation of a pharmacology-based approach to investigate the therapeutic mechanisms of Xianlinggubao (XLGB) on OA inflammation, integrating target identification, network analysis, and experimental validation

Materials & methods

Chemicals and materials

XLGB Prescription was purchased from Tongjitang Pharmacy (Guizhou, China, No. X742603), while Lipopolysaccharide (LPS, L2880) was sourced from Sigma-Aldrich (St. Louis, MO, USA). High-quality fetal bovine serum (HI-FBS, 10,099,141) and Dulbecco's Modified Eagle Medium (DMEM, 11,965,092) were obtained from GIBCO (Grand Island, NY, USA). The Griess reagent system (S0021), Cy3-labeled Goat antibody (A0516), DAPI (C1002), and CellTiter-Lumi™ Plus Detection Kit (C0065) were acquired from Beyotime (Beijing, China). TRIzol reagent (15,596,026) and real-time PCR kits (RR820 A) were purchased from Invitrogen Inc. (Carlsbad, CA, USA) and Takara Biotechnology Co., Ltd (Kusatsu, Japan), respectively. NF-κB p65 antibodies (AF0246) were obtained from Affinity (China). RAW264.7 cells were purchased from the Cell Culture Center of the Chinese Academy of Medical Sciences (Beijing, China). Methanol (1.06035.2500), acetonitrile (1.00029.2500), and formic acid (1.00264.1000), all of LC–MS grade, were sourced from Merck KGaA (Germany). All solvents used for reagent preparation were of chromatographic grade.

Collection of chemical ingredients for XLGB

All chemical components for XLGB were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) [12], the Encyclopedia of Traditional Chinese Medicine (ETCM) [13], and SymMap [14]. To identify bioactive ingredients, drug-likeness (DL) and oral bioavailability (OB) were employed as screening criteria [15]. Active components were selected based on an OB threshold of ≥ 30% and a DL threshold of ≥ 0.18.

Prediction of targets of bioactive compounds

Targets associated with the candidate bioactive compounds were identified using the STITCH, Similarity Ensemble Approach (SEA) [16], SymMap, and ChEMBL databases [17], with the “Homo sapiens” setting. Genetic data, including gene names, IDs, and organism details, were retrieved from UniProt (https://www.uniprot.org/) [18]. The identified targets are listed in Table S1.

Identification of OA and inflammation-related targets

OA-related targets were identified by screening differentially expressed genes (DEGs) from the GSE1919 dataset, as described by Ungethuem et al. (PMID: 20,858,714) [19]. The GSE1919 dataset, which includes 15 synovial tissue samples—five from patients with OA, five from patients with rheumatoid arthritis (RA), and five from healthy controls—was downloaded from the GEO database. Data were generated using the Affymetrix Human Genome U95 A Array and subsequently annotated to match gene probes to gene names. Differential gene expression analysis was performed using the"limma"package in R [20], with a cutoff of p < 0.05 and |log2 fold change (FC)|≥ 2. The "anti-inflammatory," "inflammatory," and "inflammation" datasets were then imported into the OMIM, GeneCards, and PubMed Gene databases to obtain inflammation-related targets. The FunRich [21] online tool was employed to identify overlapping DEGs and inflammation-related genes, yielding OA-inflammatory targets.

Network construction

To elucidate the molecular mechanism of XLGB in OA treatment, a multi-layered network was constructed: First, TCM compounds were linked to their respective targets, creating a compound-target network. Next, the STRING database was utilized to establish a protein–protein interaction (PPI) network encompassing OA inflammation-related and XLGB candidate targets. This interconnected network was visualized using Cytoscape 3.7.2 software, and topological properties were analyzed through the "Network Analysis" plug-in [22].

Hub gene analysis

The "CytoHubba" plug-in was employed to assess the topological characteristics of network nodes, identifying hub genes most likely to counteract XLGB’s inflammatory effects in OA. The DisGenet database [23], a platform detailing biological mechanisms, was used to extract gene and target (protein) data related to human diseases.

Functional enrichment analysis

Gene Ontology, Functional Annotation, and KEGG Pathway Analysis of gene clusters were conducted using the ClusterProfiler package in R for functional evaluation [24].

Molecular docking and dynamics simulation

Molecular docking was performed as previously outlined [25]. Briefly, the bioactive components and protein structures of the hub genes were retrieved from the PubChem and PDB databases, including PTGS2 (PDB ID: 5 F19), IL-1β (PDB ID: 6Y8M), TNF-α (PDB ID: 2 AZ5), IL-6 (PDB ID: 4O9H), and MMP-9 (PDB ID: 1ITV). Docking scores assessed the binding affinity between ingredients and predicted hub genes. Additionally, molecular dynamics (MD) simulations were conducted to confirm binding energy and ligand stability. Initial conformations for MD simulations were derived from docking results. Small molecules were optimized using ORCA [26] with DFT (B3LYP/def2-TZVP) [27–29], followed by RESP2 electrostatic potential fitting via Multiwfn [30]. Drug parameter files were generated using the GAFF force field [31] with sobtop, while protein structures were processed with PDBFixer to repair missing atoms and optimize protonation states. MD simulations were conducted in GROMACS (version 2025.0) [32] for 100 ns per protein-drug complex. Systems were parameterized using AMBER ff14SB (protein) and GAFF (drug) [31, 33], solvated in TIP3P water with 0.15 M NaCl [34, 35], and neutralized with counterions. Following steepest descent energy minimization (10,000 steps with harmonic restraints), systems underwent NVT (0–300 K over 1.0 ps) and NPT (2 ns at 300 K, 1 atm) equilibration [36]. Production runs in the NPT ensemble employed V-rescale for temperature [37], C-rescale for pressure [38], PME for electrostatic interactions [39], and LINCS for bond constraints. Binding free energies were calculated via MM/GBSA (GB-Neck2 model) [40] using 1,000 conformations (50–100 ns trajectory, 5-frame intervals), with per-residue decomposition to quantify amino acid contributions.

Preparation of XLGB extract

XLGB powder (10 g) was extracted twice with five times the amount of pure water under reflux for 30 min. The resulting extract was concentrated using rotary evaporation and then diluted to appropriate concentrations with PBS.

UPLC/TOF–MS analysis

UPLC chromatography

Separation of the extract was performed using a Thermo Vanquish UHPLC system equipped with a Zorbax Eclipse C18 column (2.1 × 100 mm, 1.8 μm). The mobile phase consisted of 0.1% formic acid aqueous solution (solvent A) and acetonitrile (solvent B). The gradient elution procedure was as follows: 0–2 min 0–5%; 2–6 min 5–30%; 6–7 min 30%; 7–12 min 78%; 12–14 min 78%; and 14–17 min 78–95%. A 10 μL sample volume was injected for analysis at a column temperature of 30 °C and a flow rate of 0.3 mL/min.

Mass chromatography

Chemical structures were analyzed using a Q-Exactive HF mass spectrometer (Thermo Fisher Scientific) with an electrospray ionization interface. The ion source settings were as follows: swelling temperature of 325 °C, peeling temperature of 350 °C, and capillary voltage set to 3.5 kV.

Cell culture and assessment of cell viability

RAW 264.7 cells were cultured in DMEM supplemented with 10% HI-FBS and maintained at 37 °C in a 5% CO₂ atmosphere. Cells (5 × 10³/well) were seeded into 96-well plates (Corning, NY, USA, 3599). After 24 h of adhesion, cells were treated with XLGB extract at concentrations of 2.5, 5, 10, and 20 µg/mL, based on preliminary dose–response experiments (0–50 µg/mL). Cell viability was assessed using the CellTiter-Lumi™ Plus Assay Kit according to the manufacturer's instructions.

Measurement of NO production

NO production was measured using the Griess reagent system, as previously described [41]. Briefly, RAW 264.7 cells (1 × 10⁴/well) were seeded into 96-well plates and treated with LPS (0.2 μg/mL). After a 24-h incubation with pre-prepared XLGB extracts, cells were centrifuged, and supernatants were collected for NO detection according to the manufacturer's guidelines.

Quantitative real-time PCR analysis

Total RNA extraction was performed using TRIzol reagent following the manufacturer's guidelines. Purity was assessed via NanoDrop 2000 (Thermo Fisher, USA), yielding A260/A280 = 1.92 ± 0.08 and A260/A230 = 2.15 ± 0.12 (n = 6). RNA concentration was quantified using the Qubit 4.0 RNA HS Assay Kit (Thermo Fisher), resulting in 382 ± 45 ng/μL. RNA integrity was confirmed with a 28S/18S rRNA band ratio > 2.0 on a 1.5% agarose gel (RIN > 8.0). Prior to PCR amplification, RNA samples underwent cDNA synthesis. Reverse transcription was performed with the PrimeScript RT Kit (RR820 A, Takara, Japan) in 20 μL reactions, each containing 1 μg RNA, 4 μL 5 × buffer, 1 μL Oligo(dT)18 primer (50 μM), and 1 μL enzyme mix. The reaction was conducted under the following conditions: initial incubation at 25 °C for 10 min for primer annealing, followed by 42 °C for 60 min for cDNA synthesis, and enzyme inactivation at 85 °C for 5 min. Gene expression was quantified using the SYBR PCR Master Mix, with primers for the target genes listed in Table 1. The thermocycling protocol was as follows: 95 °C for 3 min; 40 cycles of 95 °C for 15 s, gene-specific annealing temperature for 30 s, and 72 °C for 30 s; final extension at 72 °C for 5 min. Specificity was confirmed by melting curve analysis (60–95 °C, 0.3 °C/s). β-actin was used as an endogenous control, with triplicate technical replicates.

Table 1.

Primers used for quantitative real-time PCR

Gene	Primer	Sequence (5’−3’)	GenBank	Product length
β-actin	Forward	TGTTACCAACTGGGACGACA	AJ312193.1	165
	Reverse	GGGGTGTTGAAGGTCTCAAA
PTGS2	Forward	TGAGTACCGCAAACGCTTCTC	KR709390.1	145
	Reverse	TGGACGAGGTTTTCCACCAG
TNF-α	Forward	TAGCCAGGAGGGAGAACAGA	MH180383.1	127
	Reverse	TTTTCTGGAGGGAGATGTGG
IL-6	Forward	CTGGAGCCCACCAAGAACGA	AB028635.1	200
	Reverse	GCCTCCGACTTGTGAAGTGGT
MMP-9	Forward	CAAAGACCTGAAAACCTCCAA	KP974687.1	59
	Reverse	GGTACAAGTATGCCTCTGCCA
IL-1β	Forward	ATGCCACCTTTTGACAGTGATG	KY038171.1	141
	Reverse	GTTGATGTGCTGCTGCGAGATT

Immunofluorescence

Immunofluorescence assays were performed as previously described [41]. Briefly, cells were stimulated with LPS and treated with 20 µg/mL XLGB extract. Cells were fixed with 4.0% formaldehyde, permeabilized with 0.1% Triton X-100, and incubated overnight with an anti-p65 NF-κB antibody (1:100, 3% BSA). Following incubation with Cy3-labeled goat anti-rabbit IgG for 1 h, cells were stained with DAPI for 10 min.

Statistical analyses

Data analysis and graphing were conducted using GraphPad Prism 9 (version 9.4.0). All experiments were performed in triplicate, with data presented as the mean ± standard deviation. Group comparisons were made using one-way ANOVA and Dunnett's post-hoc test. Statistical significance was defined as P < 0.05.

Results

Screening for bioactive compounds and candidate targets in XLGB

Analyses of the TCMSP, ETCM, and SymMap databases revealed 55 distinct bioactive compounds, with 12, 8, 31, 2, 5, and 3 compounds derived from the Yinyanghuo, Zhimu, Danshen, Buguzhi, Xuduan, and Dihuang species, respectively (Table 2). Target prediction conducted using the STITCH, SEA, SymMap, and ChEMBL databases identified 475 targets associated with the bioactive compounds in XLGB. These included 273, 230, 358, 34, 157, 231, and 106 targets for Yinyanghuo, Zhimu, Danshen, Buguzhi, Xuduan, and Dihuang, respectively.

Table 2.

Summary of the bioactive compounds in the Xianlinggubao prescription (XLGB), including Yinyanghuo (YYH), Xuduan (XD), Danshen (DS), Dihuang (DH), Zhimu (ZM), and Buguzhi (BGZ)

Molecule Name	OB (%)	DL	Degree	Herb	Structure
Quercetin	46.43	0.28	217	YYH DS
Icaritin	45.41	0.44	19	YYH
8-Isopentenyl-kaempferol	37.58	0.71	13	YYH
Icariin	41.58	0.61	19	YYH
Luteolin	36.16	0.25	133	YYH DS
Chryseriol	35.85	0.27	11	YYH
C-Homoerythrinan,1,6-didehydro-3,15,16-trimethoxy	39.14	0.49	19	YYH
Kaempferol	41.88	0.24	125	YYH ZM DS
DFV	32.76	0.18	8	YYH
Linoleyl acetate	42.1	0.2	7	YYH
Poriferast-5-en-3beta-one	36.91	0.75	5	YYH
Magnograndiolide	63.71	0.19	5	YYH
Gentisin	64.06	0.21	39	XD
Beta-sitosterol/Sitosterol	36.91	0.75	105	XD DH
(E,E)−3,5-Di-O-caffeoylquinic acid	48.14	0.68	18	XD
Japonine	44.11	0.25	24	XD
Sylvestroside III	48.02	0.53	4	XD
Asperglaucide	58.02	0.52	3	ZM
Anhydroicaritin	45.41	0.44	24	ZM
Timosaponin B III_qt	35.26	0.87	2	ZM
Stigmasterol	43.83	0.76	84	ZM DH DS
(Z)−3-(4-hydroxy-3-methoxy-phenyl)-N-[2-(4-hydroxyphenyl)ethyl]acrylamide	118.35	0.26	4	ZM
Diosgenin	80.88	0.81	24	ZM
Coumaroyltyramine	112.9	0.2	5	ZM
Marmesin	50.28	0.18	9	ZM
(2S)−7-hydroxy-2-(4-hydroxyphenyl)−8-(3-methylbut-2-enyl)chroman-4-one	36.57	0.32	8	BGZ
Daucosterol	20.63	0.63	26	BGZ
Aucuboside	35.56	0.33	6	DH
1,2,5,6-tetrahydrotanshinone	38.75	0.36	14	DS
Poriferasterol	43.83	0.76	2	DS
Poriferast-5-en-3beta-ol	36.91	0.75	5	DS
Dehydrotanshinone II A	43.76	0.4	11	DS
Baicalin	40.12	0.75	80	DS
Digallate	61.85	0.26	2	DS
2-isopropyl-8-methylphenanthrene-3,4-dione	36.16	0.25	37	DS
Methylenetanshinquinone	40.86	0.23	14	DS
Danshenol B	43.67	0.21	11	DS
Danshenol A	57.95	0.56	6	DS
Salvilenone	30.38	0.38	6	DS
Cryptotanshinone	52.34	0.4	21	DS
Dihydrotanshinone I	45.04	0.36	10	DS
C09092	36.07	0.25	7	DS
Isocryptotanshi	54.98	0.39	18	DS
Isotanshinone II	49.92	0.4	16	DS
Miltirone	38.76	0.25	17	DS
1-methyl-8,9-dihydro-7H-naphtho[5,6-g]benzofuran-6,10,11-trione	34.72	0.37	11	DS
2R)−3-(3,4-dihydroxyphenyl)−2-[(Z)−3-(3,4-dihydroxyphenyl)acryloyl]oxy-pRopionic acid	109.38	0.35	5	DS
Salvilenone I	32.43	0.23	6	DS
Salviolone	31.72	0.24	6	DS
(6S)−6-hydroxy-1-methyl-6-methylol-8,9-dihydro-7H-naphtho[8,7-g]benzofuran-10,11-quinone	75.39	0.46	5	DS
Tanshindiol B	42.67	0.45	4	DS
Przewaquinone E	42.85	0.45	4	DS
Tanshinone iia	49.89	0.4	27	DS
Aloe-Emodin	36.91	0.75	29	DS
Rhein	83.38	0.24	17	DS

Building the compound-target network

A compound-target interaction network was constructed to illustrate the relationship between the bioactive compounds in XLGB and their potential targets (Fig. 2). This network comprised 530 nodes (55 compounds and 465 compound-target interactions) and 1452 edges. Network analysis revealed that bioactive compounds interacted with multiple targets, and several compounds targeted a single molecule, highlighting the multi-target therapeutic nature of XLGB. Additionally, the biological activity of the compounds exceeded 10, suggesting that the active compounds in XLGB contribute significantly to the biological network system (Table 2). Among these, quercetin (activity level 217) emerged as the most potent compound, followed by luteolin (level 133) and kaempferol (level 125), indicating their critical role in OA treatment through XLGB.

Fig. 2.

Compound-target network. Blue nodes represent predicted targets; green nodes indicate Xuduan; red nodes denote Yingyanghuo; orange nodes represent Buguzhi; pink nodes depict Danshen; gray nodes represent Dihuang; yellow nodes correspond to Zhimu

Identification of OA-inflammatory targets

Differential gene expression analysis of the GSE1919 database identified 1893 DEGs, with 973 upregulated and 900 downregulated genes (Fig. S1 A-B). Further, screening of the OMIM, GeneCards, and PubMed-Gene databases for “Homo sapiens” yielded 435 common targets, detailed in Table S2. Among these, 125 DEGs overlapped with inflammation-related genes, which were classified as OA-inflammatory genes.

PPI networks and hub genes

To elucidate the mechanisms via which XLGB modulates inflammation in OA, the interaction between XLGB targets and bioactive compounds was analyzed. The PPI network analysis identified 125 interaction nodes and 990 edges. By applying the degree principle, the top five ranked targets—PTGS2, IL-1β, TNF-α, IL-6, and MMP−9—were selected (Fig. 3). These hub genes were primarily involved in enzymatic processes and signaling pathways (Table 3).

Fig. 3.

PPI network illustrating the XLGB targets involved in inflammation in OA

Table 3.

Hub genes and their topological properties

Gene Name	Uniprot ID	Target	Target Class	Degree
TNF-α	P01375	Tumor necrosis factor-α	Signaling	69
MMP-9	P14780	Matrix metallopeptidase 9	Enzyme	59
IL-6	P05231	Interleukin-6	None	82
IL-1β	P01584	Interleukin 1 beta	None	53
PTGS2	P35354	Prostaglandin-endoperoxide synthase 2	Enzyme	60

Functional enrichment analysis

As depicted in Fig. 4A, the identified targets were predominantly enriched in biological processes (BP) related to biotic stimuli, oxidative stress, external stimuli, LPS, and bacterial-origin molecules. In cellular components (CC), the targets were associated with membrane rafts, membrane regions, outer membranes, mitochondrial outer membranes, transcription factor complexes, and serine/threonine protein kinases (Fig. 4B). In the molecular function (MF) category, targets were linked to cell adhesion molecule binding, cytokine interactions, nuclear hormone receptors, and phosphorylated proteins (Fig. 4C). KEGG pathway analysis revealed that DEGs were primarily enriched in the TNF signaling pathway, PI3 K-Akt signaling pathway, IL-17 signaling pathway, NF-κB signaling pathway, chemokine signaling pathway, HIF-1 signaling pathway, Toll-like receptor signaling pathway, p53 signaling pathway, Th17 cell differentiation, and VEGF signaling pathway (Fig. 5A). These results suggest that XLGB modulates OA inflammation through multiple signaling pathways. Among these, the NF-κB pathway plays a central role in regulating the transcription of genes involved in inflammation, immune responses, and cell differentiation. As shown in Fig. 5B, key XLGB target proteins in the NF-κB pathway include IL-1β, TNF-α, IKKα, p65, IκBα, and COX-2. IL-1β, TNF-α, and COX-2 were selected as hub genes for further validation, while the expression of IKKα, p65, and IκBα was assessed by immunofluorescence to elucidate XLGB's immune-regulatory effects.

Fig. 4.

GO pathway enrichment analysis. A Bubble chart of the top 20 biological processes (BP) associated with XLGB in OA; B Bubble chart of the top 20 cellular components (CC) associated with XLGB in OA; C Bubble chart of the top 20 molecular functions (MF) associated with XLGB in OA

Fig. 5.

KEGG pathway enrichment analysis. A Bubble chart of the 10 key signaling pathways related to XLGB in OA; B The NF-κB signaling pathway associated with XLGB treatment in OA

Molecular docking and dynamics simulation

Molecular docking analysis revealed that all bioactive compounds in XLGB bound to key targets—IL-6, PTGS2, MMP-9, TNF-α, and IL-1β—to varying extents, indicating that XLGB modulates OA inflammation through regulation of these targets (Fig. S2). Notably, as shown in Fig. 6A, (2R)−3-(3,4-dihydroxyphenyl)−2-[(Z)−3-(3,4-dihydroxyphenyl)acryloyl]oxy-propionic acid exhibited a strong binding affinity for PTGS2 (docking score = −9.057, binding energy = −152.75 kcal/mol) (Fig. 6A-a). Anhydroicaritin demonstrated strong binding to IL-6 (docking score = −9.176, binding energy = −26.64 kcal/mol) (Fig. 6A-b), IL-1β (docking score = −7.64, binding energy = −102.67 kcal/mol) (Fig. 6A-c), and TNF-α (docking score = −10.007, binding energy = −199.61 kcal/mol) (Fig. 6A-d). (2S)−7-hydroxy-2-(4-hydroxyphenyl)−8-(3-methylbut-2-enyl)chroman-4-one was found to bind to MMP-9 (docking score = −8.454, binding energy = −81.62 kcal/mol) (Fig. 6A-e). To further validate the binding energy of these compounds with their respective targets, MD simulations were performed, and the root-mean-square deviation (RMSD), a measure of the deviation of atomic coordinates from a reference structure, was used to assess the stability of the simulation system. As shown in Fig. 6B-a, RMSD analysis of the bioactive compound-key target complexes indicated that the protein–ligand systems reached equilibrium after 50 ns, suggesting stable binding between the bioactive compounds and their target atoms. Figure 6B-b illustrates the average number of hydrogen bonds formed between key targets and bioactive compounds: 2 for PTGS2, 5 for IL-6, 4.5 for IL-1β, 6 for TNF-α, and 1 for MMP-9, confirming hydrogen bond interactions between proteins and ligands. Within these simulation systems, molecules with the most negative energy values (584, 913, 719, 497, 330) played a dominant role in system stability, attributed to strong interactions such as hydrogen bonding and hydrophobic interactions. Residues A:VAL:447 (−1.70 kcal/mol), A:PHE:785 (−5.43 kcal/mol), A:ARG:596 (−2.76 kcal/mol), A:PRO:160 (−3.58 kcal/mol), and A:ILE:49 (−2.74 kcal/mol) exhibited moderately negative energy values, indicating their auxiliary role in maintaining structural integrity and potential contributions to binding (Fig. 6B-c). Collectively, these results demonstrate that the bioactive compounds in XLGB exhibit strong binding affinities with key targets (PTGS2, IL-1β, TNF-α, IL-6, MMP-9), further validating the reliability of the predicted interactions.

Fig. 6.

Molecular docking and dynamics simulation of bioactive compound-hub target interactions. A Molecular docking of bioactive compounds with hub target: (a) (2R)−3-(3,4-dihydroxyphenyl)−2-[(Z)−3-(3,4-dihydroxyphenyl)acryloyl]oxy-propionic acid-PTGS2 (docking score = −9.057); (b) Anhydroicaritin-IL-6 (docking score = −9.176); (c) Anhydroicaritin-IL-1β (docking score = −7.64); (d) Anhydroicaritin-TNF-α (docking score = −10.007); (e) (2S)−7-hydroxy-2-(4-hydroxyphenyl)−8-(3-methylbut-2-enyl)chroman-4-one-MMP-9 (docking score = −8.454); B Molecular dynamics simulation of bioactive compound-hub target interactions: (a) Root-mean-square deviation (RMSD); (b) Intramolecular H-bond plots; (c) Energy contributions of residues in the molecular dynamics simulation

Compounds present in the XLGB extract

UPLC/TOF–MS analysis was performed to identify the compounds present in XLGB extracts (Fig. 7A–B). Using retention times, primary and secondary mass spectrometry data, and screening relevant literature, 34 compounds were identified, including coniferaldehyde, atenolol acid, 3-phenylpropanoic acid, 3,5-dihydroxy-2-naphthoic acid, ikarisoside C, diphylloside B, lithospermic acid, salvianolic acid, epimedin C, maslinic acid, ikarisoside B, psoralen, anhydroicaritin 3-rhamnosyl, neobavaisoflavone, flemistrictin E, isobavachalcone, bavachinin, cryptotanshinone, tanshinone IIA, estra-1,3,5(10)-trien-3-ol, piscidic acid, mussaenosidic acid, sweroside, 3-acetyl-7-beta-D-glucopyranosyl-oxycoumarin, cycloolivil, epimedin A1, epimedin C, ovemotide, noranhydroicaritin-3-rhamnosyl-rhamnoside, timosaponin A-III, xanthohumol, corylin, isobavachin, and estradiol (Table 4). Many of these compounds, including neobavaisoflavone, cryptotanshinone, and tanshinone IIA, have been previously reported in Salvia miltiorrhiza and related medicinal herb studies [42, 43]. However, compounds such as cycloolivil and timosaponin A-III have not been widely reported in the literature, and their presence in XLGB suggests that they may contribute to its pharmacological properties. Further studies are required to fully elucidate their potential roles and confirm their significance in the therapeutic effects of XLGB.

Fig. 7.

UPLC/TOF–MS analysis of XLGB extract. A Positive mode chromatograms; B Negative mode chromatograms

Table 4.

MS/MS data in (±) ESI modes and the identities of bioactive compounds in XLGB

NO	Ingredient	Time (min)	m/z	Mode	Formula
1	Coniferaldehyde	4.82	178.06	Positive	C10H10O3
2	Atenolol acid	4.88	267.14	Positive	C14H21NO4
3	3-Phenylpropanoic acid	5.78	150.06	Positive	C9H10O2
4	3,5-Dihydroxy-2-naphthoic acid	6.09	204.04	Positive	C11H8O4
5	Ikarisoside C	7.05	824.27	Positive/Negative	C38H48O20
6	Diphylloside B	7.21	808.27	Positive	C38H48O19
7	Lithospermic acid	7.74	538.11	Positive	C27H22O12
8	Salvianolic acid	7.75	718.15	Positive	C36H30O16
9	Epimedin C	8.41	822.29	Positive	C39H50H19
10	Maslinic acid	9.14	472.35	Positive	C30H48O4
11	Ikarisoside B	9.53	662.21	Positive/Negative	C32H38O15
12	Psoralen	10.03	186.03	Positive	C11H6O3
13	Anhydroicaritin 3-rhamnosyl	10.51	660.24	Positive/Negative	C33H40O14
14	Neobavaisoflavone	11.25	322.11	Positive/Negative	C20H18O4
15	Flemistrictin E	11.91	324.13	Positive	C20H20O4
16	Isobavachalcone	12.49	324.13	Positive	C20H20O4
17	Bavachinin	12.80	338.15	Positive	C21H22O4
18	Cryptotanshinone	13.29	296.14	Positive	C19H20O3
19	Tanshinone IIA	14.46	296.12	Positive	C19H18O3
20	Estra-1,3,5(10)-trien-3-ol	14.93	256.18	Positive	C18H24O
21	Piscidic acid	3.31	256.06	Negative	C11H12O7
22	Mussaenosidic acid	4.82	376.14	Negative	C16H24O10
23	Sweroside	5.80	358.13	Negative	C16H22O9
24	3-acetyl-7-beta-D-glucopyranosyloxycoumarin	6.10	366.10	Negative	C17H18O9
25	Cycloolivil	6.13	376.15	Negative	C20H18O9
26	Epimedin A1	7.85	838.29	Negative	C39H48O20
27	Epmedin C	8.40	822.29	Negative	C39H50O19
28	Ovemotide	9.15	973.51	Negative	C46H71N9O14
29	Noranhydroicaritin 3-rhamnosyl-rhamnoside	9.73	646.23	Negative	C32H38O14
30	Timosaponin A-III	11.59	740.43	Negative	C39H64O13
31	Xanthohumol	11.89	354.15	Negative	C21H22O5
32	Corylin	11.91	320.10	Negative	C20H16O4
33	Isobavachin	12.49	324.14	Negative	C20H20O4
34	Estradiol	13.65	272.18	Negative	C18H24O2

Effect of XLGB extracts on cell viability and NO production

To determine the optimal concentration of XLGB extract, RAW264.7 cells were treated with various concentrations (20, 10, 5, 2.5 μg/mL) of the extract for 24 h, and cell viability was assessed using the CellTiter-Lumi™ Kit. No significant differences were observed between the DEX group and XLGB-treated groups across all concentrations (p > 0.05), indicating the absence of cytotoxicity, even at the highest dose of 20 μg/mL. In contrast, LPS treatment notably reduced cell viability, demonstrating both cytotoxic and inflammatory effects in RAW264.7 cells (p < 0.05). This outcome aligns with the established pro-inflammatory role of LPS, commonly used to induce inflammation in OA cell models (Fig. 8A). Additionally, LPS stimulation resulted in a significant increase in NO production compared to the control group (p < 0.01), confirming successful induction of inflammation. In contrast, XLGB extract treatment significantly reduced LPS-induced NO production in a dose-dependent manner. At the highest concentration (20 μg/mL), XLGB significantly decreased NO production compared to the LPS group (p < 0.01). Similar reductions were observed at 10 μg/mL and 5 μg/mL (p < 0.05), with the lowest concentration (2.5 μg/mL) also leading to a significant decrease in NO production (p < 0.05), indicating XLGB's anti-inflammatory effect even at lower doses (Fig. 8B).

Fig. 8.

Effects of XLGB extract on RAW264.7 cells. A Effect of XLGB extract on cell viability of RAW264.7 cells (n = 3); B Effect of XLGB extract on NO production (n = 6); C PTGS2 mRNA expression; D IL-1β mRNA expression; E IL-6 mRNA expression; F MMP-9 mRNA expression; G TNF-α mRNA expression (n = 3) (p* < 0.05, **p < 0.01)

Effect of XLGB extract on hub genes in RAW264.7 cells

To assess the role of hub genes in the anti-inflammatory effects of XLGB, mRNA expression levels were measured in RAW264.7 cells treated with LPS. Cells were exposed to varying concentrations of XLGB extract, LPS (0.2 μg/mL), and DEX (100 mM) as a positive control. As shown in Fig. 8, LPS treatment significantly upregulated the expression of hub genes, confirming the successful induction of inflammation in RAW264.7 cells. In contrast, DEX, a known anti-inflammatory agent, effectively downregulated these genes, validating its use as a positive control. XLGB extract significantly downregulated the expression of PTGS2 (Fig. 8C), IL-1β (Fig. 8D), IL-6 (Fig. 8E), MMP-9 (Fig. 8F), and TNF-α (Fig. 8G) in a dose-dependent manner (*p < 0.05, **p < 0.01) following LPS stimulation. At a concentration of 20 μg/mL, XLGB exhibited a significant downregulation of the hub genes, comparable to the effect of DEX. At lower concentrations (5 μg/mL and 2.5 μg/mL), the inhibitory effects were less pronounced but still statistically significant. Notably, even at the lowest concentration (2.5 μg/mL), XLGB significantly reduced IL-1β mRNA levels. These results indicate that while LPS upregulated hub gene expression, XLGB effectively suppressed their expression, thereby attenuating inflammation by modulating these key genes.

NF-κB nuclear translocation

Figure 9 highlights the differential expression of p65 within the NF-κB signaling pathway. To explore the mechanism through which XLGB modulates OA inflammation, the intracellular localization of p65 was examined. In untreated cells, the p65 subunit (red) was predominantly located in the cytoplasm, while in the normal group, it was found in the nucleus (blue). LPS treatment induced nuclear translocation of p65, thereby activating NF-κB transcriptional activity. This effect was counteracted by XLGB treatment (20 μg/mL), which inhibited the nuclear translocation of p65 (Fig. 9). These results suggest that the NF-κB signaling pathway regulates inflammation and tight junctions following combined LPS and XLGB treatment.

Fig. 9.

Nuclear localization of the p65 subunit of NF-κB revealed by immunofluorescence

Discussion

OA is a chronic inflammatory condition characterized by progressive joint degeneration and subchondral bone remodeling. The primary goal of OA treatment is symptom management [7]. In TCM, OA is classified as a bone callus [44]. XLGB, a formulation prepared by the Miao minority group [45], is frequently prescribed for OA [46] due to its minimal side effects, diverse dosage forms, affordability, and proven efficacy. A multicenter, randomized, open-label, controlled trial demonstrated that XLGB effectively alleviates pain, improves symptoms, and enhances function in patients with OA [45]. This study aimed to elucidate the anti-inflammatory mechanism of XLGB in OA treatment by constructing an anti-inflammatory compound-OA target network, performing functional enrichment and molecular docking analyses, and validating these findings through molecular experiments.

Fifty-five bioactive components of XLGB, including flavonoids, coumarins, saponins, alkaloids, sugars, and terpenes, met the screening criteria. The compound-target network analysis identified quercetin, luteolin, and kaempferol as key bioactive compounds with significantly higher degree values, suggesting their potential as major therapeutic targets. Quercetin, a widely recognized flavonoid, is known for its potent anti-inflammatory, antioxidant, and other beneficial properties, such as lowering blood pressure and dilating coronary arteries [47]. Research indicates that quercetin has a chondroprotective role by inhibiting the inflammatory response and apoptosis of chondrocytes, modulating synovial macrophage polarization, and fostering a pro-chondrogenic environment for chondrocytes [48]. Luteolin, a natural flavonoid found in various plants, exhibits multiple pharmacological effects, including anti-inflammatory, anti-allergy, anti-tumor, antibacterial, and antiviral activities [49]. Fei et al. [50] demonstrated that luteolin attenuates IL-1β-induced inflammation by inhibiting the production of NO, PGE2, TNF-α, MMPs, and the phosphorylation of NF-κB and p65. Kaempferol (OB = 41.88%, DL = 0.24, degree = 125), a bioflavonoid commonly found in fruits and vegetables, significantly inhibits IL-1β-induced pro-inflammatory mediators through suppression of NF-κB signaling [51].

Analysis of the PPI network revealed 125 interaction nodes and 990 edges, highlighting the key targets associated with the anti-inflammatory effects of XLGB in OA. Topological property analysis identified five hub genes—PTGS2, IL-1β, TNF-α, IL-6, and MMP-9—with the highest degree values, indicating their potential significance as anti-inflammatory targets in XLGB's treatment of OA. Inflammatory cytokines such as IL-1β and TNF-α bind to receptors on cell membranes, increasing MMP expression, which triggers chondrocyte apoptosis and cartilage destruction [52]. IL-6, a key pro-inflammatory cytokine, plays a pivotal role in OA development and progression, explaining its elevated levels in patients with OA [53]. IL-6 also mediates the effects of IL-1β and TNF-α, further exacerbating OA progression [54]. PTGS2 regulates prostaglandin levels, and prostaglandin-2 increases vascular permeability, promotes inflammatory cell infiltration, and stimulates IL-6 production from chondrocytes via the NF-κB pathway [55]. Additionally, molecular docking and dynamics simulations confirmed that core bioactive ingredients, such as anhydroicaritin, exhibited strong binding affinities with key targets, such as TNF-α and IL-6 (Fig. 6), further supporting the reliability of the predictive results.

Functional enrichment analysis indicated that all targets in the PPI network were associated with IL-17, TNF, and NF-κB signaling pathways. IL-17, a potent inflammatory factor, contributes to bone metabolism and disease. It modulates osteoblast differentiation and promotes bone degradation by stimulating osteoblasts and bone stromal cells [56]. Liu et al. [57] suggested that IL-17 expression correlates with the severity of KOA pain, and blocking the IL-17 signaling pathway may alleviate OA-related pain. The expression of caspase and CXCL10 was positively correlated with OA progression. Fu et al. [58] observed significantly higher caspase-3 activity in OA groups compared to sham groups. Furman et al. [59] further demonstrated that upregulation of CXCL10 plays a critical role in chronic inflammation and tissue damage following its elevation in OA cartilage. TNF, a major mediator of inflammation, downregulates inflammatory factor expression in OA synovial fibroblasts, suggesting that inhibition of the TNF signaling pathway could represent a promising strategy for OA treatment [60]. The NF-κB pathway plays a central role in the development and persistence of OA, with previous studies indicating that NF-κB mediates PTGS2 to regulate prostaglandin E2 (PGE2) production. Activated by pro-inflammatory cytokines like IL-1β and TNF-α, NF-κB forms an inflammatory feedback loop. The TNF-α/IL-1β-NF-κB-IL-6 axis constitutes a cascade signaling network that drives cartilage degeneration and synovial inflammation in OA [61]. Ni et al. also demonstrated that fibroblast-like synoviocytes modulate OA progression by activating the NF-κB signaling pathway and promoting synovitis [62].

RT-PCR and immunofluorescence analyses confirmed that XLGB extract modulated the expression of predicted genes (PTGS2, IL-1β, TNF-α, IL-6, MMP-9) and impacted the NF-κB signaling pathway. LPS-induced activation of RAW264.7 macrophages, key contributors to the inflammatory response, stimulated the production of pro-inflammatory cytokines, including IL-6 and TNF-α. LPS is widely used to model inflammation-related diseases and to induce inflammatory conditions in cells. RAW264.7 macrophages play a central role in the inflammatory development of OA. LPS treatment elevated the expression of the identified hub genes (PTGS2, IL-1β, TNF-α, IL-6, MMP-9), while XLGB treatment reduced their expression, demonstrating that XLGB antagonized the inflammatory effects in RAW264.7 cells in a dose-dependent manner. Wu et al. reported that Xianlinggubao, a traditional remedy for OA, exerts its effects by inhibiting chondrocyte apoptosis and inflammatory responses through the JNK and PI3 K/AKT/NF-κB signaling pathways [63]. In contrast, this study combines bioinformatics analyses with experimental validation, revealing that XLGB reduces OA-related inflammation and suppresses the NF-κB pathway, corroborating previous findings from a different perspective. Our study provides significant insights supporting the clinical application of XLGB in OA treatment.

This study has several limitations. Regarding research design and methodology, the reliance on existing databases (e.g., TCMSP, ETCM) for screening active ingredients and targets may lead to the exclusion of certain XLGB constituents and novel inflammatory targets. Future research will incorporate high-resolution mass spectrometry and metabolomics to more comprehensively identify chemical entities in XLGB. While the RAW264.7 macrophage cell line served as a useful model for validating inflammatory targets, it does not fully replicate the intricate interactions present in the OA joint microenvironment. Therefore, future studies will utilize primary synovial fibroblasts derived from patients with OA or 3D organoid models to improve experimental fidelity. Additionally, the current findings, primarily based on in vitro experiments, lack in vivo validation. To address this gap, OA animal models (e.g., collagenase-induced or surgical models) will be employed in combination with micro-CT and histopathological analysis to assess XLGB's effects on cartilage degradation and bone remodeling. From a translational medicine perspective, XLGB—a multi-component traditional Chinese medicine—interacts through a complex network of constituents influencing its efficacy. Component deletion experiments or orthogonal design studies will be necessary to isolate and identify key active ingredients. Furthermore, existing clinical data do not fully account for differential efficacy across OA subtypes, highlighting the need for multi-center, stratified clinical studies.

Future research will focus on three key areas. 1. Animal Model Validation: Collagenase-induced (MIA-OA) or surgically induced (e.g., DMM) mouse OA models will be used to assess XLGB's in vivo effects on cartilage degradation, synovial inflammation, and bone remodeling. Micro-CT, histopathology (hematoxylin–eosin/Safranin O staining), and biomechanical analyses will provide quantitative assessments of joint structure and function. Gene knockout models (e.g., NF-κB p65 conditional knockout mice) and fluorescent reporter systems will be employed to explore the regulation of signaling pathways. 2. Clinical Translation: Multi-center, randomized, double-blind, placebo-controlled RCTs (following CONSORT guidelines) will involve patients with knee/hand OA to evaluate the long-term effects on pain (VAS), function (WASOC), and inflammation (hs-CRP, IL-6). Proteomic and metabolomic profiling of biosamples (serum, synovial fluid) will identify biomarkers responsive to treatment, enabling precision stratification. 3. Mechanism & Technology: Single-cell transcriptomic sequencing (scRNA-seq) will be utilized to uncover XLGB’s regulatory networks in OA joint cell subpopulations (macrophages, synovial fibroblasts, chondrocytes), revealing immunometabolic reprogramming. Nanodelivery systems (e.g., liposomal/exosomal carriers) will enhance the bioavailability of active ingredients in joints while minimizing systemic exposure. Collectively, these strategies will deepen mechanistic understanding and enhance the translational potential of XLGB in OA treatment.

Conclusions

This study, by integrating network pharmacology with experimental validation, comprehensively elucidated how XLGB suppresses OA inflammation through the synergistic modulation of PTGS2, IL-1β, TNF-α, IL-6, MMP-9, and the NF-κB pathway. This provides a theoretical foundation for the multi-component, multi-target synergistic effects of TCM compounds, paving the way for developing low-side-effect treatment strategies for OA. Future research will focus on three primary objectives: first, evaluating XLGB’s in vivo efficacy in OA using animal models; second, exploring XLGB’s impact on immune cell subsets within the joint microenvironment using single-cell sequencing technology; and third, conducting multi-center clinical trials to assess XLGB’s long-term safety and patient-stratified efficacy. These forthcoming studies will build upon the findings of the current research while providing clear guidance for future investigations.

Abbreviations

ADME

Adsorption, distribution, metabolism, and excretion

AR

Androgen receptor

BP

Biological processes

CC

Cellular components

CM

Chinese Medicine

DEGs

Differentially expressed genes

DL

Drug-likeness

DMM

Dorsal Medical Meniscectomy

EGF

Epidermal growth factor

EGFR

Epidermal growth factor receptor

ETCM

Encyclopedia of Traditional Chinese Medicine

FC

Fold change

GO

Gene Ontology

ILs

Interleukins

KEGG

Kyoto Encyclopedia of Genes and Genomes

MIA-OA

Monosodium lodoacetate-induced OA

MD

Molecular dynamics

Micro-CT

Micro-Computed Tomography

OA

Osteoarthritis

OB

Oral bioavailability

PGE2

Prostaglandin E2

PPI

Protein–protein interaction

RA

Rheumatoid arthritis

RCT

Randomized Controlled Trial

RMSD

Root-mean-square deviation

scRNA-seq

Single-cell RNA sequencing

SEA

Similarity ensemble approach database

TCM

Traditional Chinese medicine

TCMSP

Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform

VAS

Visual Analogue scale

Authors’ contributions

Naiqiang Zhu and Bin Chen conceived and designed the study. Jingyi Hou performed the investigation, formal analysis and writing- original draft. Yubo Li performed the methodology, software, data curation and visualization. Ning Yang performed the experimental validation. Yu Zhang and Guiyun Ma wrote and revised the manuscript and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

This study was supported by the Hebei Natural Science Foundation (H2022406038) and National Natural Science Foundation of China (82305055).

Data availability

All datasets presented in this study are included in the article/Supplementary Materials.

Declarations

Ethics Statement

Not applicable.

Competing interests

The authors declare no competing interests.