Rheumatoid arthritis (RA) is a systemic polyarticular chronic autoimmune joint disease that affects 0.5%–1.0% of the global population (Huang et al., 2021). RA primarily affects the musculoskeletal system, with joints commonly experiencing severe ramifications, including local inflammation, cartilage deterioration, and bone erosion. These cascading effects ultimately lead to joint damage and physical disability (Mueller et al., 2021). There are multiple immune function-related pathways correlated with RA that include the JAK/STAT signaling pathway, the NF-κB signaling pathway, the AMPK signaling pathway, and the TLR signaling pathway (Li et al., 2019a; Cheng et al., 2020; Li et al., 2020). Contemporary RA treatment for encompass several approaches, such as utilizing composite markers for gauging disease activity, implementing targeted therapy strategies, and employing a range of conventional, biological, and innovative non-biological anti-rheumatic drugs for managing the condition (Smolen et al., 2016). The majority of patients’ experience alleviation of associated symptoms following treatment; however, there remain individuals who do not respond to the existing therapy. Hence, there is an urgent need for novel treatment approaches.

There are limited pharmaceutical options available for the clinical treatment of RA. Nevertheless, significant progress has been made due to ongoing advancements in disease-modifying anti-rheumatic drugs (DMARDs) that have resulted in notable success in preventing and mitigating disease activity in RA patients. However, despite these advances, response rates remain constrained, underscoring the need for innovative targets and therapies (Huang et al., 2021). In recent years, there has been a growing emphasis on the role of traditional Chinese medicines (TCMs) and their extracts in disease treatment (Tang et al., 2021). These medicines include the Shuanghuanglian injection, the Danshen injection, the Tacrolimus injection, and other preparations that are effective compounds extracted and separated from Coptis Chinensis, Salvia miltiorrhiza, Panax ginseng, and Ophiopogon japonicus (He and Li, 2021). Whether it is a compound preparation with undisclosed ingredients or a small molecule drug with well-defined active components, both have supplied compelling evidence-based support for the utilization of TCM for the clinical transformation of RA treatment. This evidence indicates that the integration of TCM into a comprehensive treatment approach for RA patients can contribute significantly to achieving disease remission.

The Juan-Bi decoction (JBD), initially documented in the “Yang’s Household Recipe” authored by Yang Kui during the Southern Song Dynasty, primarily addresses symptoms such as “rheumatic discomfort, body aches, neck and arm pain, mobility challenges, cold extremities, and lumbar heaviness with weakened legs, muscles, and veins.” This prescription stands as a classical clinical remedy for the management of RA (LI et al., 2021). An investigation involving a combination of the Juan-Bi Tongluo decoction with methotrexate tablets and enteric-coated sulfasalazine tablets has demonstrated that this treatment effectively alleviates the primary clinical symptoms in patients with early RA, particularly those experiencing damp-heat arthralgia. Furthermore, it contributes to improvements in various laboratory physiological and chemical markers. These beneficial effects may be attributed to a reduction in adipokine levels and the regulation of the inflammatory immune response (Xiao and Ye, 2021). The outcomes from the combined approach of TCM and western anti-rheumatism medication for RA have indicated superior therapeutic efficacy, particularly in active patients. This improvement could potentially be linked to the amelioration of the erythrocyte sedimentation rate (ESR), rheumatoid factor (RF), and C-reactive protein (CRP) levels, as well as the regulation of MMP-3 and TIMP-1 levels in both serum and joint fluid (Bian, 2018). Furthermore, the combination of Qing-Re JBD with methotrexate has demonstrated a notable therapeutic impact in addressing damp-heat obstruction type RA. This treatment approach effectively ameliorates the clinical symptoms in patients, decreases the levels of serum α1-AGP and TL1A, and mitigates the inflammatory response, all without an increase in adverse reactions (Wang, 2021).

Previous pharmacological investigations, both in vitro and in vivo have indicated that the JBD exhibits promising protective and therapeutic properties. Moreover, the Qing-Re JBD has displayed significant clinical improvements in patients with RA characterized by cold-dampness obstruction. This treatment effectively reduces the ESR and CRP levels. The underlying therapeutic mechanism appears to involve the suppression of pro-inflammatory cytokines TNF-α and IL-1β, a decrease in the MMP-3 levels, and an upregulation of TIMP-1 expression (Li et al., 2019b). Rat experiments have revealed that the Cheng-Shi JBD exerts its anti-inflammatory properties by elevating serum IL-35 levels and suppresses the release of IL-17 (Lei et al., 2019). In addition, the JBD additionally modulates immune balance by diminishing natural killer cells and fostering T-regulatory cells in rats with collagen-induced arthritis (CIA). Furthermore, it lowers the cAMP levels in T lymphocytes in rats with adjuvant-induced arthritis (Wang et al., 2020).

The JBD is a renowned TCM formulation utilized for RA treatment. However, our understanding of its effectiveness primarily relies on TCM knowledge. There has been limited research on the active components and potential mechanisms of action of the JBD and its constituent herbs. The integration of network pharmacology and systems pharmacology offers a promising avenue for precise and effective therapeutic interventions. This approach leverages synergistic multi-compound networks and the repurposing of drugs, bypassing the need for extensive drug discovery and expediting clinical translation. The use of robust and meticulous methodologies can help researchers analyze potential synergies and facilitate more comprehensive assessments for future applications (Panossian et al., 2021; Nogales et al., 2022). In this study, we used a systematic approach that involves constructing a compound-target-pathogenic gene network using the chemical constituents found in the herbal components of the JBD. We used a mathematical model to optimize this network and successfully identified and screened the active ingredients within these herbal components. Subsequently, we validated these findings through experimentation. This comprehensive methodology presents a promising avenue for utilizing the JBD in RA treatment. It not only furnishes a theoretical foundation but also serves as a valuable methodological reference for the screening of natural products in TCM.

Botanical drugs and dosage details in the JBD study were sourced from the Chinese Pharmacopeia 2020 (National Pharmacopoeia Commission, 2020). Taxonomic validation of all botanical drugs was conducted using the Plants of the World Online (https://powo.science.kew.org/) and the Chinese Medicinal Material Images Database (https://library.hkbu.edu.hk/electronic/libdbs/mmd/) (Table 1). Information about the chemical components within these botanical drugs was gathered from various publicly accessible Traditional Chinese Medicines (TCMs) databases, such as the Traditional Chinese Medicine Information Database (TCMID, http://bidd.group/TCMID/) (Chen et al., 2006), and the Traditional Chinese Medicine and Active Ingredient Database (TCMAID, http://www.organchem.csdb.cn/scdb/main/tcm_introduce.asp). The chemical structures were prepared and converted into canonical SMILES using the OpenBabel Toolkit (O Boyle et al., 2008).

Table 1. Herbs and dosage information in JBD.

The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and physicochemical characteristics of all compounds were predicted using the ADMETlab 2.0 web server (https://admetmesh.scbdd.com/) (Xiong et al., 2021). Under Lipinski’s rule of five (Pollastri, 2010), potential active compounds were required to fulfill the following criteria: (1) molecular weight (MW) ≤ 500; (2) number of hydrogen bond donors (nHD) ≤ 5; (3) number of hydrogen bond acceptors (nHA) ≤ 10; (4) logarithm of the n-octanol/water distribution coefficient (logP) ≤ 5; and (5) the number of rotatable bonds (nRot) ≤ 10. Furthermore, potential active compounds needed to meet the following additional conditions: (6) human oral bioavailability (OB) ≥ 30%, (7) Caco-2 permeability (Caco-2) ≥ 0.4, (8) topological polar surface area (TPSA) ≤ 60, and (9) no hepatotoxicity.

The potential targets of the potential active compounds were predicted using the following web servers: the similarity ensemble approach (SEA) search server (https://sea.bkslab.org/) (Keiser et al., 2007), HitPickV2 (http://www.hitpickv2.com/) (Hamad et al., 2019), and SwissTargetPrediction (http://www.swisstargetprediction.ch/index.php) (Daina et al., 2019). The union set of the prediction results was defined as the potential targets.

The RA pathological genes were searched and downloaded from the DisGeNet database (https://www.disgenet.org/) (Pinero et al., 2020). Genes with literature reports were defined as RA pathological genes. The intersection of targets and pathogenic genes was defined as essential common proteins (ECPs).

For the network construction methods, see our previous publication (Xu et al., 2023a). Briefly, we retrieved the interactions between targets and pathogenic genes and extracted documented protein-protein interactions from the BioGrid database (https://thebiogrid.org/) (Oughtred et al., 2019). We combined this with previous compound-target prediction results, and the compound-target-pathological gene (C-T-P) network was constructed and visualized using Cytoscape software (Kohl et al., 2011).

Transcriptome data from the macrophages of RA patients were downloaded from the National Center for Biotechnology-Gene Expression Omnibus (NCBI-GEO) database (https://www.ncbi.nlm.nih.gov/geo/) with accession identification numbers of GSE10500 and GSE97779. Data preprocessing, integration, and global normalization were conducted using our previously described method (Li et al., 2016a; Li et al., 2016b). The differential expression analysis was conducted using the empirical Bayesian algorithm in the limma package (Ritchie et al., 2015) in R statistical software (https://www.r-project.org/). Significantly differentially expressed genes were defined as absolute values of the log2-transformed fold change (logFC) > 1 and an FDR p-value <0.05.

The C-T-P network was optimized using the MOO model. The purpose of optimization was to extract target genes that were strongly associated with compounds and pathogenic genes and were dysregulated in RA patients. Details of the MOO model and network optimization can be found in a previous report (Xu et al., 2023b).

The GO terms of biological process, cellular component, molecular function, and human gene information were downloaded from the QuickGO database (https://www.ebi.ac.uk/QuickGO/) (Binns et al., 2009). The reference human genes and pathways were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.kegg.jp/) (Kanehisa and Goto, 2000). The GO terms and KEGG pathways with fewer than 10 genes were removed. The enrichment analysis was performed using the hypergeometric test. An false discovery rate (FDR)-corrected p-value ≤0.05 was considered significantly enriched.

The degree model, closeness model, and betweenness model (Yang et al., 2021) were used to optimize the constructed C-T-P network and compared with the MOO model. For details see the previous publication (Xu et al., 2023a). The optimization performance of the different models was compared from the five aspects: (1) the coverage of essential common proteins; (2) the coverage of the top 100 enriched KEGG pathways; (3) the coverage of the top 1,000 enriched GO biological processes (BP); (4) the average regulating intensity; and (5) the cumulative differential expression. Genes in the optimized network in the different models were used to perform the KEGG pathway and GOBP enrichment analyses, and the reference KEGG pathways and Gene Ontology biological processes (GOBPs) were enriched using essential common proteins (ECPs). The average regulating intensity was calculated as the mean Pearson’s correlation coefficient for the gene-gene pairings in the optimized network. The cumulative differential expression was defined as the sum of the absolute values of the logFC of ECPs in the optimized network.

The arthritis index (AI) was graded as following criteria: 0, normal joints; 1, slight swelling or a digital red spot; 2, red skin and slight swelling in the ankles and feet; 3, moderate swelling and erythema; 4, severe swelling and erythema involving the entire posterior claw or precipitation (Kollias et al., 2011).

The mice were anesthetized and sacrificed. The wrist, knee, ankle, and elbow joints were collected and fixed with 4% paraformaldehyde for 24 h and decalcified with 10% ethylenediaminetetraacetic acid (EDTA). After processing using conventional histological procedures, such as dehydration, transparency, wax immersion, and embedding, the paraffin-embedded tissue sections were stained by hematoxylin and eosin (H&E) staining (Sigma). The H&E staining score evaluation criteria were as follows: it could be divided into five degrees of zero to four points according to the edema, structural damage, and inflammatory cell infiltration of lesions, from light to heavy. The score was measured (Liu et al., 2022).

For safranin O/fast green staining, the paraffin-embedded slices were deparaffinized, sequentially immersed in xylene I for 20 min, xylene II for 20 min, absolute ethanol I for 5 min, absolute ethanol II for 5 min, 75% alcohol for 5 min and washed with tap water. The slices were stained with fast green staining for 5–10 min, dried to discard the excess dyeing solution until the cartilage was colorless, soaked in a differentiation solution, and washed in tap water. The slices were stained with the safranin staining solution for 15–30 s and dehydrated quickly with absolute ethanol. The slices were made transparent with xylene for 5 min, mounted with neutral gum, observed, and photographed under the microscope (Zhang et al., 2021a).

The RAW264.7 cells or synovial tissues were homogenized and lysed in radioimmunoprecipitation assay (RIPA) buffer (Sigma-Aldrich) that contained a protease inhibitor (Roche, Mannheim, Germany). A total of 50 μg protein isolated from the RAW264.7 cells or synovial tissues was separated using 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to a polyvinylidene difluoride (PVDF) membrane (Millipore, Billerica, MA, USA). The membranes were incubated with the following primary antibodies: CYP17A1 (ab125022, Abcam), HSD17B3 (ab126228, Abcam), TLR4 (ab13556, Abcam), p-MAPK (AF4001, Affinity), MAPK (BF8015, Affinity), p-p65 (#11011, SAB) and p65 (#48676, SAB). Subsequently, a horseradish peroxidase (HRP)-conjugated secondary antibody was added for the 1-h incubation. The blots were developed using the ECL Western blot Kit (LabLead, E1050) on the gel imaging system (ChampgelTM 5,000 plus, SINSAGE, China). The exposure time for blots ranged from 100 ms to 3 s. Film density was measured using ImageJ densitometry software and was normalized against glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (#60004-1-Ig, Proteintech) (Liu et al., 2022).

The total RNA was extracted using Trizol (Invitrogen) according to the manufacturer’s protocol. One milligram of RNA was reverse transcribed into cDNA using avian myeloblastosis virus (AMV) reverse transcriptase (Qiagen, Vento, Netherlands) with an RNase inhibitor and oligo d(T) primer at 40°C for 50 min followed by heating at 90°C for 5 min. Then, 1 μL of the reverse-transcript was added to a 30 μL PCR mixture for 40 cycles. Each cycle included 93°C for 30 s and 54°C for 60 s and used Taq polymerase. Negative controls consisted of an equal volume of water substituted for the volume of RNA in the reverse transcriptase (RT) reaction. The normalization of mRNA expression data for sample-to-sample variability in RNA input, RNA quality, and reverse transcription efficiency was achieved by a the comparison between the copy numbers of the target gene and the housekeeping gene, and GAPDH. The relative gene expression was measured using the 2−ΔΔCT method. Primer sequences are shown in Table 2.

Table 2. Oligonucleotide primers for the RT-qPCR.

This study involved the collection of a total of 1,252 compounds derived from eight different herbs (Bai Shao, Dang Gui, Fang Feng, Gan Cao, Huang Qi, Jiang Huang, Qiang Huo, and Sheng Jiang). The pharmacological and ADMET properties of these compounds were assessed and predicted (Supplementary Table S1). Following our screening criteria, we identified and retained 274 potentially active compounds. Among these, the numbers of screened potential active compounds found in Bai Shao, Dang Gui, Fang Feng, Gan Cao, Huang Qi, Jiang Huang, Qiang Huo, and Sheng Jiang were 12, 33, 46, 27, 1, 16, 39, and 100, respectively (Figure 1; Supplementary Table S2). In addition, it is worth noting that there were 48 compounds that were common to two or more of these herbs.

Figure 1. Compounds after ADMET screening in herbs in JBD. Lines between dots indicate herbs that share these compounds.

Our methodology involved a series of steps. Initially, duplicate compounds were removed, after which target prediction was conducted using three different tools: SEA, HitPickV2, and SwissTargetPrediction, resulting in predictions of potential targets for the remaining 175 compounds. These predictions indicated that these compounds could potentially interact with a range of 16–178 targets (Supplementary Table S3). Furthermore, we collected a total of 2,766 distinct pathological genes associated with rheumatoid arthritis (Supplementary Table S4). These targets and pathological genes were cross-referenced with the BioGRID database to acquire protein-protein interaction data. Subsequently, we merged the compound-target network with the protein-protein interaction network to create the comprehensive C-T-P network. The original C-T-P network encompassed (Ru et al., 2014), 535 compound-target pairs and 46,838 protein-protein interactions (Figure 2A). Notably, 438 proteins were found to be both target proteins and pathogenic genes. Subsequent KEGG pathway and GOBP enrichment analyses highlighted their involvement in multiple signaling pathways linked to human diseases (Supplementary Tables S5, S6).

Figure 2. The original (A) and optimized (B) C-T-P network. The orange, pink, green, and cyan points indicate potential active compounds, predicted targets, RA pathogenic genes, and ECPs, respectively. Compounds and genes with a high degree of expression were highlighted and labelled.

The original C-T-P network was refined using the MOO method. The optimized C-T-P network now comprises 10,239 compound-target pairs and 24,091 protein-protein interactions, incorporating 175 compounds, 1,085 predicted targets, and 2,081 rheumatoid arthritis pathological genes (with 438 genes shared between the target and pathological gene categories), as depicted in Figure 2B. Notably, the top 20 genes with the highest degrees in both the original and optimized networks have been labeled. Of these top genes, 13 are common to both the target genes and pathological genes (AR, APP, BRD4, CTNNB1, ESR1, ESR2, GSK3B, HDAC1, HSP90AA1, MDM2, NTRK1, PARP1, and NTRK1).

The results revealed varying degrees of overlap in the essential genes, with 438, 272, 259, and 273 common genes identified in the MOO model, degree model, closeness model, and betweenness model, respectively. Similarly, for the top 100 enriched KEGG pathways, there were 90, 86, 85, and 83 shared pathways among these four models, respectively. Furthermore, in the case of the top 1,000 enriched GOBPs, the numbers of overlapping terms were 775, 538, 521, and 542 in the MOO model, degree model, closeness model, and betweenness model, respectively. In addition, we evaluated the average regulating intensity and cumulative differential expression values, finding that these metrics were consistently higher in the MOO model compared with the other three models (Figure 3). These findings collectively indicated that the optimization performance of the MOO model surpassed that of the other models.

Figure 3. Optimization performance comparison of MOO model with other models. Essential common genes were defined as the overlapped target genes and RA pathogenic genes. Top 100 KEGG pathways and top 1000 GOBP based on FDR p-values were chose for comparison.

The interaction intensity flow on the pathogenic genes associated with each screened compound was calculated (Supplementary Table S7). Remarkably, the cumulative impact of 15 specific compounds on these pathogenic genes accounted for 90% of all active compounds (Figure 4). The details of these compounds, which originate from Bai Shao (one compound), Dang Gui (three compounds), Fang Feng (seven compounds), Gan Cao (four compounds), Huang Qi (one compound), Qiang Huo (three compounds), and Sheng Jiang (eight compounds), are elaborated in Table 3. Notably, among these compounds, JBD00018 (stearic acid) and JBD01030 (eugenol) were FDA-approved drugs, while the remaining 13 compounds are natural ingredients derived from botanical sources. The cumulative interaction intensity flow of these screened compounds on the enriched pathways suggested that these 15 compounds primarily affect metabolic pathways, immune pathways and signaling transduction pathways (Figure 5; Supplementary Table S8). Next, we verified the effectiveness of these compounds using cell and animal experiments.

Figure 4. The interaction intensity on pathogenic genes of compounds in the optimized network. The cumulative intensity represents the combined interaction intensity on pathogenic genes of that compound and all existing active compounds. The increased intensity of each compound represents the cumulative intensity minus the interaction intensity on pathogenic genes of the existing active compounds. A dashed horizontal line indicates the 90% interaction intensity on pathogenic genes of all active compounds.

Table 3. Final screened compound information in JBD.

Figure 5. The cumulative interaction intensity flow of pathways. All pathways were sorted by descending cumulative intensity flow and the top 20 pathways were showed.

The effects of the compounds on cell viability and inflammatory response were verified using LPS-induced RAW.264.7 cells. Because some compounds cannot be purchased or have no Chemical Abstracts Service (CAS) registry number, six selected compounds were utilized in the experiment: JBD01030 (eugenol), JBD00018 (stearic acid), JBD00198 (isoamylbenzene), JBD01230 (dihydrocapsaicin), JBD00978 (p-hydroxyphenethyl anisate), and JBD00233 (bornyl acetate). The secretion of IL-6, IL-1β, and TNF-α significantly increased in the LPS-induced RAW264.7 cells (Figure 6A). We then examined the cell viability of the RAW264.7 cells after treatment with JBD01030 (eugenol), JBD00018 (stearic acid), JBD00198 (isoamylbenzene), JBD01230 (dihydrocapsaicin), JBD00978 (p-hydroxyphenethyl anisate) and JBD00233 (bornyl acetate) (0, 1.56, 3.125, 6.25, 12.5, 25, 50 and 100 μM). There was no significant change in the cell viability of RAW264.7 cells after being treated with stearic acid, dihydrocapsaicin, or p-hydroxyphenethyl anisate (Figure 6B). Administration of isoamylbenzene and bornyl acetate significantly decreased the cell viability of RAW264.7 cells (Figure 6B). NO production significantly decreased after treatment with eugenol, stearic acid, and bornyl acetate at 10 and 25 μM in the LPS-induced RAW264.7 cells (Figure 6C). The administration of 3-methybutylbenzene at 5, 10, and 25 μM significantly decreased the NO production in LPS-induced RAW264.7 cells (Figure 6C). Moreover, treatment with dihydrocapsaicin and p-hydroxyphenethyl anisate at 25 μM decreased the NO production in LPS-induced RAW264.7 cells (Figure 6C). Intriguingly, administration of p-hydroxyphenethyl anisate at 1, 5 and 10 μM increased the NO production in LPS-induced RAW264.7 cells (Figure 6C). The above results showed that isoamylbenzene and bornyl acetate (BAC) both reduced cell viability and inhibited inflammation. Studies have shown that BAC has antioxidant and anti-inflammatory properties in different types of tissues and cells (Yang et al., 2018). Isoamylbenzene has only a benzene ring and a hydrophobic chain segment, so its water solubility is poor. It is an oily liquid, and it lacks other functional groups, such as hydroxyl, sulfhydryl, sulfonic acid and carboxyl. Oxygen in the ether bond can increase the water solubility of the drug, and aminos and amides can form hydrogen bonds with the target. In addition, the presence of benzene rings alone may present a risk of cancer. Therefore, we used BAC for the animal experiment verification.

Figure 6. Effects of screened compounds on the inflammatory responses in LPS-induced RAW.264.7 cells (A) The IL-6, IL-1β, and TNF-α concentration in the cellular supernatant was examined by ELISA (n = 4). (B) The cells were cultivated with increasing concentrations of eugenol, stearic acid, isoamylbenzene, dihydrocapsaicin, p-hydroxyphenethyl anisate, and bornyl acetate (0, 1.56, 3.125, 6.25, 12.5, 25, 50 and 100 μM). Cell viability was measured by CCK-8 assay (n = 4). (C) NO production of RAW264.7 cells was measured after being treated with eugenol, stearic acid, isoamylbenzene, dihydrocapsaicin, p-hydroxyphenethyl anisate and bornyl acetate (1, 5, 10 and 25 μM, n = 4). The experiments were performed three times, and the error bars represent means ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ns: no significance.

To verify the efficacy of BAC against RA, a CIA mouse model, which is widely used to clarify the mechanisms of RA and to explore the potential therapeutic targets, was established. The mice were then treated with MTX, the first-line treatment for RA, and two dosing regimens of BAC (Figure 7A).

Figure 7. Bornyl acetate alleviates joint damage in CIA mice. (A) The timeline of constructing CIA mouse models, MTX treatment, and FZD treatment in this study (n = 6). (B) Arthritis index was examined after MTX and JBD treatment (n = 6). (C) Microcomputer tomography images of the knee joint of mice (n = 6). (D) Bone surface/volume ratio was calculated (BS/BV, mm-1) (n = 6). (E) H&E staining of wrist, knee, elbow, and ankle joint tissues in each group (n = 6). (F–I) Histopathological scores were measured independently by three pathologists blinded to the experiment (n = 6). (J) Wrist, knee, elbow, and ankle joint tissues were stained with safranin O-fast green (n = 6). (K–N) Modified Mankin’s scores of wrist, knee, elbow, and ankle joint tissues were measured (n = 6). The experiments were performed three times, and the error bars represent means ± SD. *p < 0.05, **p < 0.01, ***p < 0.001.

The arthritis index (AI) of the CIA mice was higher than that of any other group. Furthermore, the AI of the MTX-treated CIA mice was lower than BAC-treated CIA mice. Importantly, the AI in the CIA + BAC-H group was lower than in the CIA + BAC-L group (Figure 7B). The effect of MTX or BAC on the knee joints of mice was measured by using micro-CT. The bone surface/volume (BS/BV) ratio of the CIA group was obviously higher than that of the control group. Administration of MTX or BAC significantly decreased the BS/BV ratio. Intriguingly, BAC at a high concentration was more effective in decreasing the BS/BV ratio (Figures 7C,D).

To further explore the effects of MTX and BAC on the CIA mice, the wrist, knee, elbow, and ankle joints of the mice were subjected to histological analysis by H&E and Safranin O staining. Compared with the control group, the joint structure was incomplete, the cartilage surface was rough, and a large number of inflammatory cells had infiltrated the joint tissues in the CIA group. Administration of MTX or BAC alleviated synovial hyperplasia and decreased the severity of cartilage damage and inflammatory cell infiltration (Figure 7E–N). Intriguingly, BAC at a low concentration was more effective in decreasing histopathological scores and modified Mankin’s scores of wrist and knee joints. BAC at a high concentration was more effective in decreasing histopathological scores and modified Mankin’s scores of the elbow and ankle joints of mice (Figure 7E–N).

The bioinformatics analysis above revealed that the screened compounds exerted their most significant impact on the biosynthesis of steroid hormones. We selected several markers in steroid hormone biosynthesis to verify the effect of BAC on the levels of these proteins (including CYP17A1, HSD17B3, TLR4, MAPK, and p65). The expressions of CYP17A1, HSD17B3 and TLR4 increased in the CIA mice compared with the control mice. The phosphorylation of MAPK and p65 both significantly increased in the CIA mice compared with the control mice. However, administration of MTX and BAC decreased the expressions of these proteins (Figure 8). Importantly, BAC at a high concentration was more effective in decreasing the expressions of CYP17A1 and HSD17B3 and the phosphorylation levels of MAPK and p65 compared with BAC at a low concentration (Figure 8).

Figure 8. BAC deactivates steroid hormone biosynthesis in CIA mice. (A–D) Protein expression levels of CYP17A1, HSD17B3, TLR4, p-MAPK, MAPK, p-p65 and p65 were examined by Western blot (n = 6). (E–G) mRNA expression of CYP17A1, HSD17B3, TLR4, p-MAPK, MAPK, p-p65 and p65 was detected by RT-qPCR (n = 6). The experiments were performed three times, and the error bars represent means ± SD. *p < 0.05, **p < 0.01.

It has been reported that the JBD can treat with chronic lower back pain, external humeral epicondylitis, synovitis of the hip in children, ankylosing spondylitis, gout, and other bone diseases. In addition, it decreases inflammatory cytokines, such as TNF-α and IL-1, in the serum of rats induced by Freund’s adjuvant (Xiao and Ye, 2021). Then Zini Juan-Bi Tongluo decoction combined with MTX and salazine sulfopyridine relieves the main clinical symptoms of early RA with damp-heat arthralgia and reduces the level of adipokines in the serum (Xiao and Ye, 2021). The CIA mouse model is the most commonly studied autoimmune model of RA because it shares several pathological features with RA, including synovial hyperplasia, cartilage degradation, mononuclear cell infiltration, and bone destruction (Qin et al., 2022). In this study, we successfully established the CIA mouse model. We demonstrated that BAC or MTX treatment effectively inhibited joint destruction and inflammatory cell infiltration in