Introduction
Rheumatoid arthritis (RA) is an autoimmune disease that causes chronic inflammation in the joints, leading to pain, swelling, and joint deformity1. The development of RA is complex; it involves the activation of specialized cells known as fibroblast-like synoviocytes (FLS), which are located in the joints of affected patients. These cells produce pro-inflammatory cytokines and matrix metalloproteinases that contribute to joint tissue destruction and the chronic inflammation associated with RA2,3. In the early stage of RA, patients may be positive for rheumatoid factor (RF) and anti-cyclic citrullinated peptide antibody (anti-CCP), but no obvious clinical symptoms have yet appeared. At this time, the abnormal activation of the immune system may have begun, but it has not caused significant joint damage. The joint may be slightly swollen or uncomfortable, but there is usually no obvious joint damage or dysfunction4. The immune system of confirmed RA patients is more abnormal, and the positive rate of RF and anti-CCP antibodies is higher, which is related to the disease activity and often accompanied by obvious joint injury. Imaging examination (such as X-ray and MRI) can show joint erosion and bone destruction5.
The exact cause of RA remains unclear, but it is believed to arise from the complex interaction of genetic, environmental, and immune system factors. In RA patients, the immune system is overactive, resulting in joint inflammation and damage. Specifically, FLS in RA patients over-proliferate and produce excessive inflammatory cytokines, which promote inflammatory responses and bone destruction6,7. Furthermore, large numbers of CD4+ T cells and Th17 cells are present in the joints of RA patients, producing various inflammatory factors that exacerbate joint inflammation and injury8,9. Numerous studies have emphasized the critical role of various signaling pathways and molecular players in RA pathogenesis and progression. In the JAK-STAT pathway, an important signal transduction pathway in cells, the combination between cytokines with their receptors activates JAK kinaseto phosphorylate STAT protein, which then enters the nucleus and regulates the gene expression related to immune response and inflammation10,11. In patients with RA, the over-activated JAK-STAT signaling pathway promotes the production of a variety of inflammatory cytokines, such as IL-6 and IFN-γ, which play a key role in the inflammatory process, leading to persistent joint injury and inflammatory reaction12, 13–14. By inhibiting the JAK-STAT signaling pathway, the production of inflammatory factors can be effectively reduced, alleviating RA symptoms and preventing disease progression.
Recent research progress shows that targeted synthetic Disease-Modifying Anti-Rheumatic Drugs (tsDMARDs) and biological Disease-Modifying Anti-Rheumatic Drugs (bDMARDs) are important in the treatment of RA, but these treatments, which suppress the immune system, also increase the risk of other diseases.This part can consider the anti-inflammatory and antioxidant properties of polyphenols, which may help to alleviate the side effects of tsDMARDs and bDMARDs. Traditional Chinese medicine has great advantages in the treatment of RA because of its small side effects, multi-targets and multi-effects15. Geniposide (GE), a natural compound derived from the fruit of Gardenia jasminoides, is known for its various biological activities such as anti-inflammatory, antioxidant, and immunomodulatory effects16. GE has demonstrated therapeutic effects in treating diabetes, chronic hepatitis, ulcerative colitis, atherosclerosis, psoriasis, and other diseases. This indicates that GE may ameliorate various immune-mediated inflammatory diseases17, 18, 19, 20–21. For instance, in ulcerative colitis, GE can alleviate symptoms of colitis and colon barrier injury, suppress the expression of pro-inflammatory cytokines, and block the activation of the NF-κB signaling pathway in colon tissue. Additionally, GE enhances lipid peroxidation in colitis and helps restore redox homeostasis22. GE has a protective effect on the cardiovascular system, reducing blood pressure, improving heart function, and combating atherosclerosis23. It achieves these effects by inhibiting inflammatory responses, reducing oxidative stress, and protecting the function of vascular endothelial cells24. The diverse pharmacological effects of GE open new avenues for research and therapeutic strategies in modern medicine. However, its impact in the treatment of RA should be further elucidated, so as to its mechanism of action and its safety and efficacy in RA clinical treatment.
This study integrated network pharmacology with experimental validation to offer a comprehensive view of GE’s potential mechanisms in RA. The potential targets of GE were identified by using network pharmacology methods, the anti-inflammatory effects of GE were validated through cellular experiments, and its molecular mechanisms were clarified in RA treatment based on the response of those potential targets identified. The achievements of this study will help to further develop the treatment of rheumatoid arthritis and solve the limitations of current treatment methods. The flowchart of this study is presented in Figure 1.
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Fig. 1
Flow chart of the analysis process in the study.
Results
Intersecting targets of GE and RA
Based on draft guidelines for evaluating network pharmacology methods, using the 2D structural formula of GE as illustrated in Fig. 2A, a network pharmacology prediction of GE was conducted. Potential targets for the effects of GE were gathered from the databases such as TCMSP (8 targets), SwissTargetPrediction (41 targets), Pharmmapper (296 targets), and Batman (1 target). After culling duplicate targets, we obtained 330 potential targets for drug action. Detailed target information is provided in Supplement Table 1. The differentially expressed genes (DEGs) in RA were filtered from the GSE55235 dataset using the’limma’package. A total of 1324 genes were screened under the conditions of |log (FC)|≥ 1 and p value < 0.05, including 740 up-regulated and 584 downregulated genes. The volcano plot visualization of differential genes in the GSE55235 dataset is shown in Fig. 2B. The Venn diagram revealed 53 common targets shared between GE drug targets and RA DEGs (Fig. 2C), and a heat map showing the differential expression of which is presented in Fig. 2D.
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Fig. 2
Intersecting targets of GE and RA.. (A) 2D structure of drug GE. (B) Volcano plot representing differentially expressed genes in the datasets GSE55235: grey indicates genes with no difference, red represents upregulated genes, and blue denotes downregulated genes. (C) Intersection targets of disease and drug. (D) Heatmap showcasing of differentially expressed genes. The x-axis represents samples from the two datasets, while the y-axis displays the differentially expressed genes. Red indicates high expression and blue signifies low expression.
Screening of key targets of GE against RA
Next, 53 potential anti-RA targets were analyzed by using the STRING database to create a PPI network (Supplement Figure 1). The reason why we consider the threshold value of 0.4 in PPI as the limiting value is mainly because when we adjust the threshold value to 0.7, we find that there are four scattered sub-networks after processing in string database, and eight genes in one sub-network are interconnected, which will affect the final screening result if we directly abandon it. Moreover, when we adjust the threshold value to 0.9, many sub-networks appear, and each sub-network has fewer genes. The PPI network map was imported into Cytoscape 3.9.0 for visualization (Figure 3A). Figure 3 shows the identified subnetwork with 12 key targets: EGFR, MMP-9, CCL5, PPARG, STAT1, HCK, SYK, MAPK8, CTSB, RAC2, JAK2, and TYMS. These targets are considered to play significant roles in the action of GE against RA, and further details about the 12 key targets are available in Supplement Table 2.
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Fig. 3
Screening key targets of GE against RA. (A) The PPI network of 53 common targets is drawn by cytoscape. The darker the color and the larger the shape, the higher the Degree value. (B) Hub genes were screened from the PPI network using the Betweenness (BC), Closeness (CC), Degree (DC), and Network (NC) methods. (C) Subnetwork of the PPI network of 12 hub targets.
GO and KEGG enrichment analysis
The GO analysis identified 769 significantly enriched GO terms (p < 0.01 with Benjamini-Hochberg correction), comprising 650 biological processes, 40 cell components, and 79 molecular functions. The top 20 GO entries were further screened, as shown in Figure 4A-C. In the biological process (GO: BP) category, the top-ranked terms include leukocyte migration, leukocyte cell-cell adhesion, and response to peptides. In the cell component (GO: CC) category, the top-ranked terms include the extracellular matrix containing collagen, membrane microdomains, and the vesicle lumen. In the molecular function (GO: MF) category, the top-ranked terms include peptidase activity, serine hydrolase activity, and protein kinase activity for serine, threonine, and tyrosine. To identify potential signaling pathways, an analysis of the KEGG pathways were conducted. Figure 4D shows the top 20 significantly enriched pathways (p value < 0.01). A list of genes involved with the 20 selected pathways is provided in Supplement Table 3. Several targets have been found to be associated with signaling pathways related to the pathogenesis and prognosis of RA, including IL-17, JAK-STAT, and TNF signalin pathways..
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Fig. 4
Functional analysis of differentially expressed genes. (A-C) The GO enrichment analysis results showed that leukocyte migration, leukocyte cell–cell adhesion was significantly enriched in RA. (D) KEGG results showed that IL-17 and JAK-STAT signaling pathways were significantly enriched in RA.
The ROC diagnostic curve analysis of the key targets
The ROC curve (Figure 5) was generated to further assess the diagnostic potential of the 12 key genes. The results demonstrated that the AUC values were as follows: CCL5 and SYK had values of 1.0, PPARG, STAT1, and TYMS had values of 0.99, HCK had 0.98, CTSB had 0.97, EGFR, MMP-9, and MAPK8 had 0.96, RAC2 had 0.95, and JAK2 had 0.93. This finding suggests that the key genes possess high accuracy and specificity in distinguishing RA samples from those of normal controls.
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Fig. 5
ROC curves of the 12 key genes in the datasets GSE55235 for RA diagnosis.
Molecular docking
Molecular docking analysis was conducted to evaluate the interactions between the drug and its target, to predict their binding affinity, and to validate the network analysis results. In this analysis, GE acted as a ligand, while 12 key genes served as receptors: EGFR (PDB: 2RGP), MMP-9 (PDB: 5TH6), CCL5 (PDB: 6AEZ), PPARG (PDB: 2ZVT), STAT1 (PDB: 3WWT), HCK (PDB: 2HK5), SYK (PDB: 3SRV), MAPK8 (PDB: 3ELJ), CTSB (PDB: 1GMY), RAC2 (PDB: 2W2T), JAK2 (PDB: 5L3A), and TYMS (PDB: 1YPV). The molecular docking and binding energy results for GE and the 12 key target proteins were as follows: GE-EGFR (−8.06 kcal/mol), GE-MMP-9 (−9.24 kcal/mol), GE-CCL5 (−8.97 kcal/mol), GE-PPARG (−9.24 kcal/mol), GE-STAT1 (−7.51 kcal/mol), GE-HCK (−9.73 kcal/mol), GE-SYK (−9.22 kcal/mol), GE-MAPK8 (−11.6 kcal/mol), GE-CTSB (−9.12 kcal/mol), GE-RAC2 (−8.34 kcal/mol), GE-JAK2 (−9.41 kcal/mol), and GE-TYMS (−8.47 kcal/mol), all less than −5 kcal/mol, demonstrating good binding activity. PyMOL was utilized to create 3D binding pattern maps, as shown in Figure 6, to show the potential interaction between GE and the 12 targets.
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Fig. 6.
3D mode diagram of molecular docking. (A) EGFR and GE. (B) MMP-9 and GE. (C) CCL5 and GE. (D) PPARG and GE. (E) STAT1 and GE. (F) HCK and GE. (G) SYK and GE. (H) MAPK8 and GE. (I) CTSB and GE. (J) RAC2 and GE. (K) JAK2 and GE. (L) TYMS and GE.
Cell proliferation and toxic effects of GE on RA-FLS
FLS play a crucial role in the pathogenesis of RA. The Healthy FLS (HFLS) and RA-FLS cells needed for the experiment were cultured in a 37 ℃ incubator with 5% CO2. The effect of GE on RA-FLS cell proliferation was assessed by using the MTT assay. Additionally, the toxic effects of various concentrations of GE on HFLS were tested. After the MTT reagent reacted with the cells, the optical density (OD) value, measured at 490 nm using a microplate reader, was proportional to the number of cells. The MTT experiment results demonstrated a dose-dependent of RA-FLS response to different concentrations of GE (Figure 7B). Notably, a concentration of 200 μM was cytotoxic to HFLS after 48 hours (Figure 7A). The results of IC50 assay showed that the 50% inhibitory rate of the drug was 67.47 μM at 24 h and 31.76 μM at 48 h (Supplement Figure 2). Based on the MTT experiment results, we selected cells treated with GE at doses of 0, 25, 50, and 100 μM for subsequent experiments.
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Fig. 7
Effect of different concentrations of GE on cell viability in RA-FLS and HFLS cells for 24 h and 48 h. (A) GE added HFLS produced cytotoxicity at 200 μM for 24 h and 48 h. (B) GE inhibited the proliferation of RA-FLS cells in a dose-dependent manner. Compared with the blank control group, ####P < 0.0001. Compared with the TNF-α stimulus group, ****P < 0.0001.
Effect of GE on the levels of inflammatory factors in RA-FLS
The inflammatory response is a key pathological process in the development of RA, and measuring inflammatory factors can help determine the severity of the disease and also reflect the cell response to drugs. As shown in Figure 8, the ELISA results indicate that after TNF-α stimulation, the levels of IL-17, IL-8, TNF-α, MMP-3, and MMP-9 in RA-FLS significantly increased, as shown in classical inflammatory responses. Following the addition of varying concentrations of GE, their levels exhibited a concentration-dependent decrease. These results indicated that GE can inhibit the production of inflammatory factors in RA-FLS cells, which supports its potential application in the treatment of RA.
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Fig. 8
Effect of different concentrations of GE on inflammatory cytokine release in RA-FLS cells. After 24 h of GE treatment, the expression level of inflammatory factors in the supernatant of RA-FLS cells was determined by ELISA assay. (A) IL-8. (B) IL-17. (C) TNF-α. (D) MMP-3. (E) MMP-9. Compared with the blank control group, ###P < 0.001. Compared with the TNF-α stimulus group, **P < 0.01, ****P < 0.0001.
Effects of GE on mRNA expression of key targes identified by network pharmacology in RA-FLSs
Network pharmacology was used to screen and identified 12 key targets relating with both RA and GE, including EGFR, MMP-9, CCL5, PPARG, STAT1, HCK, SYK, MAPK8, CTSB, RAC2, JAK2, and TYMS. RT-qPCR was used to detect changes in mRNA expression to assess whether GE influenced the expression of these key targets and could potentially exert anti-RA effects (The primer sequences for 12 key targets are listed in Supplement Table 4). RT-qPCR results indicated that after co-incubation of RA-FLS with TNF-α for 24 hours, EGFR, MMP9, CCL5, STAT1, SYK, MAPK8, and JAK2 were significantly up-regulated, while PPARG was significantly downregulated,the other four targets showing no statistically significant change (Figure 9). However, the addition of the drug GE along with TNF-α stimuli reversed the mRNA expression effects of TNF-α on these 8 out of 12 key targets. This validates the results of network pharmacology, suggesting that GE affects the mRNA expression of the eight key targets in RA-FLS.
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Fig. 9
GE inhibits the mRNA expression of key targets in TNF-α-induced RA-FLS. (A-L) The effect of GE on the mRNA levels of core genes in RA-FLS stimulated by TNF-α. Compared with the blank control group, #P < 0.05, ##P < 0.01, ###P < 0.001, ####P < 0.0001. Compared with the TNF-α stimulus group, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.
Effects of GE on JAK-STAT signaling pathways in RA-FLSs
The KEGG pathway is enriched in IL-17 and JAK-STAT signaling pathways. The JAK-STAT signaling pathway is an important cytokine transduction pathway. By inducing the production of specific cytokines, such as IL-17, the JAK-STAT signaling pathway enhances the autoimmune response and leads to joint inflammation. Activation of this signaling pathway can promote the proliferation of synovial fibroblasts and other immune cells, resulting in exacerbated pathological changes. The JAK-STAT pathway also influences the differentiation of T cell subsets, particularly in regulating the generation of Th17 cells, which are believed to play a critical role in the pathogenesis of RA. Therefore, we examined the effect of GE on the JAK-STAT signaling pathway using a western blot assay (The raw data with three repetitions is shown in Supplement Figure 3). Figure 10 shows that GE could significantly inhibit P-JAK1 and P-STAT1 protein expression in RA-FLS cells and reduce the levels of inflammatory factors, thus diminishing the inflammatory response in RA. The above results suggest that GE may exert its anti-RA effects by inhibiting the phosphorylation of the JAK-STAT signaling pathway proteins.
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Fig. 10
GE inhibits the activation of JAK/STAT pathway in TNF-α-induced RA-FLS. FLS for p-JAK1 and p-STAT1 were starved in 10% FBS medium, pretreated with GE and 20 ng/ml TNF-α for 48 h before collection. The phosphorylation of JAK1 and STAT1 in FLS were measured by western blot. Compared with the blank control group, #P < 0.05, ##P < 0.01, ###P < 0.001, ####P < 0.0001. Compared with the TNF-α stimulus group, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.
GE alleviated inflammation in CIA mice
In order to explore the relieving effect of GE on inflammatory symptoms of CIA mice, we established CIA model and injected GE intraperitoneally for 14 days (The specific modeling process is shown in Figure 11A). We observed the state, weight and degree of joint swelling of mice. After the model was established, the activity of mice decreased and the coat became dark. Compared with the model group, the low-dose (GE-L) and high-dose GE (GE-H) groups could delay the weight loss of CIA mice and reduce the swelling of their hind paws (Figure 11B-C). The results of ELISA serum test (Figure 11D) showed that, compared with the model group, the low-dose and high-dose GE group could reduce the content levels of pro-inflammatory factors IL-17, TNF-α, IL-8, MMP-3 and MMP-9, and increase the content level of anti-inflammatory factor IL-10. The therapeutic effect of high concentration drug group is close to that of positive drug MTX group. HE staining results suggested that GE could significantly alleviate the symptoms of cartilage destruction and inflammatory cell immune infiltration in CIA mice (Figure 11E). These data show that GE plays a significant role in alleviating the inflammatory symptoms of CIA mice.
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Fig. 11
GE attenuated the severity of arthritis in CIA mice. (A) In vivo experiment flow chart. (B) Observation of foot paw swelling in CIA model mice induced by GE. (C) Effects of GE on body weight and hind paw swelling of CIA model mice. (D) ELISA was used to detect the effect of GE on the inflammation of CIA model mice. (E) HE staining results of CIA mice joints after GE treatment. Black arrows indicate cartilage destruction, and asterisks indicate inflammatory cell infiltration. Compared with Control group, ####P < 0.0001. Compared with CIA group, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.
Discussion
RA is a chronic inflammatory disease that primarily affects the synovium of joints, causing pain, swelling, and dysfunction. Current research indicates that the pathogenesis of RA is closely linked to autoimmune reactions, which involve various immune cells and inflammatory factors, including T cells, B cells, and tumor necrosis factor-α (TNF-α)9,25. Although biological agents (like TNF inhibitors) and targeted therapies (such as Janus kinase inhibitors) have shown significant progress in treating RA, they still face challenges that need to be overcome, including severe side effects and high costs, and some patients do not respond well to current therapies. This study aims to explore the potential role and mechanisms of GE, a natural compound derived from the fruit of Gardenia jasminoides, in treating RA. This research provides new insights and evidence for its therapeutic application. The main findings highlight GE’s potential targets in inhibiting RA-FLS proliferation and inflammatory cytokines produce, as well as its regulatory role in the JAK-STAT signaling pathway. Finally, the in vivo therapeutic effect of GE in alleviating inflammation was further determined in CIA mice.
Through network pharmacology, this study identified 330 targets related to GE and 1,324 targets related to RA, finally discovered 53 potential GE targets for RA. The PPI network was constructed using the STRING database, and 12 out of 53 key targets were identified: EGFR, MMP9, CCL5, PPARG, STAT1, HCK, SYK, MAPK8, CTSB, RAC2, JAK2, and TYMS. Molecular docking verification showed that GE and the 12 identified key targets exhibit strong binding affinity and stable conformation. Among them, MAPK8 docking with GE exhibit s the highest binding affinity with energy of −11.6 kcal/mol, showing the possibility of drug action superior to other targets. GE may provide new treatment options for RA, but its efficacy and safety need to be verified by further experiments. Therefore, we assessed the biological activity of GE at the cellular level and quantitatively analyzed the impact on the mRNA expression of identified targets using qRT-PCR, and the results preliminarily validated the biological significance of the molecular docking results. Future research can focus on molecular dynamics simulation and surface plasmon resonance to evaluate its clinical application prospect more comprehensively26. GO and KEGG enrichment analysis revealed 650 biological processes (BP), with top-ranked terms including leukocyte migration, leukocyte cell-cell adhesion, and response to peptides. The results indicate that the potential targets of GE against RA are primarily associated with immune-mediated inflammatory reactions. Notably, the JAK-STAT signaling pathway shows greater reliability and a higher number of enriched targets. It is an important signal transduction pathway in cells, which is mainly involved in the response of cells to cytokines and growth factors, thus regulating biological processes such as immune response, inflammatory response, and cell proliferation27,28. In patients with RA, inflammatory cytokines (such as IL-6, IFN-γ) activate JAK family members (such as JAK1, JAK2, JAK3, TYK2) by binding to their corresponding cell surface receptors, triggering the phosphorylation and dimerization of STAT protein. The activated STAT dimer is transferred to the nucleus, where it combines with specific DNA sequences to promote the gene expression of inflammatory factors, leading to synovial hyperplasia, persistent inflammatory reactions, and joint destruction29, 30, 31–32. The abnormal activation of the JAK-STAT signaling pathway plays an important role in the pathological progression of RA, promoting not only the persistence of inflammatory reactions but also the abnormal activation and differentiation of immune cells33. In-depth study of the molecular mechanisms and interactions of these signaling pathways will help reveal the pathogenesis of RA and provide a theoretical basis for developing new therapeutic strategies. Therefore, it is worth to further investigate the role of the JAK-STAT signaling pathway in the potential mechanisms of GE against RA through experiments.
FLSs are crucial in RA as they maintain the stability and balance of the intra-articular environment. Activated FLSs are the predominant effector cells in the progression of RA34. In RA pathogenesis, the FLS phenotype changes and shows invasive behavior similar to that seen in cancer. Additionally, FLSs enhance the stress-induced microenvironment, which recruits other immune cells and ultimately contributes to pannus formation35. Reports indicate an interaction between FLSs in the synovium and synovial T lymphocytes in RA patients36,37. T lymphocytes activate FLSs, stimulating the production and secretion of inflammatory mediators such as IL-6, IL-8, and prostaglandin E2 (PGE2)38. Our experiment showed that the treatment of GE at 25, 50, and 100 μmol/L for 24 and 48 hours not only inhibited the proliferation of RA-FLSs but also inhibited the expression of inflammatory factors in a drug concentration-dependent manner. GE inhibits five key inflammatory cytokines, namely IL-8, IL-17, TNF-α, MMP-3, and MMP-9, emphasizing its important role in regulating the immune response. Regulating these cytokines is critical in RA pathophysiology, as they promote synovial proliferation, angiogenesis, and the destruction of bone and cartilage through various inflammatory pathways39. The results of this study are consistent with previous studies on the inhibitory effect of GE on inflammatory factors. Furthermore, GE has no effect on the proliferation of normal HFLS in the experimental concentration range, indicating that GE has no obvious toxic and side effects on normal cells and had the potential to serve as an effective and safe drug for treating RA.
RT-qPCR demonstrated that GE inhibits the mRNA expression of EGFR, MMP-9, CCL5, STAT1, SYK, MAPK8, and JAK2 mRNA in RA-FLS cells. In RA, cytokines like interferon-γ (IFN-γ) bind to their receptors to activate JAK1 and JAK2 from the JAK family. This activation subsequently triggers STAT1. Phosphorylated STAT1 dimerizes and translocates to the nucleus, initiating the transcription of inflammation-related genes40. Over-activation of STAT1 is associated with an increase in the expression of numerous pro-inflammatory genes, including interferon-stimulating genes, chemokines, and MHC class II molecules. The products of these genes further recruit and activate immune cells, creating a vicious cycle that exacerbates joint inflammation41,42. The activation of STAT1 is crucial in the pathology of RA and accelerates disease progression. The significant inhibition of phosphorylated JAK1 (P-JAK1) and STAT1 (P-STAT1) in RA-FLS cells treated with GE indicates that GE effectively disrupts the JAK-STAT signaling pathway, which is essential for inflammatory responses in RA. By attenuating this pathway, GE may reduce the activation and proliferation of immune cells, thereby alleviating the autoimmune response and joint inflammation associated with RA. This mechanistic insight into GE’s action provides a strong rationale for its potential use in developing new immunomodulatory therapies for RA.
The comprehensive approach of this study, i.e., combining network pharmacology, molecular docking, and experimental validation, offers a robust framework for understanding the multifaceted effects of GE and underscores its therapeutic promise in RA management. Despite the promising findings, this study has several limitations that must be addressed. Firstly, we only conducted the binding ability analysis using molecular docking. At the cellular and animal levels, we assessed the cytotoxic side effects of GE on the viability of normal HFLS and its ameliorative effect on the body weight of CIA mice. Further studies could analyze the histological staining results of the heart, liver, spleen, lung, and kidney in CIA mice treated with the drug. Secondly, while we identified potential targets and pathways through network pharmacology and experimental validation, the precise molecular mechanisms by which GE exerts its effects on RA remain to be elucidated. Additionally, regarding whether GE necessarily exerts its anti-inflammatory effect through the JAK/STAT pathway, we could introduce a group of drug treatments with pathway activators or overexpress STAT1 via transfection, to further investigate this at the cellular and animal levels. Rescue experiments could be conducted to clarify the dependence of the drug’s anti-inflammatory effect on the signaling pathway or specific targets. Further studies involving animal models and clinical trials are necessary to confirm the therapeutic potential of GE against RA and to understand its pharmacokinetics and pharmacodynamics in a physiological context.
GE has been extensively researched, trying to confirming its anti-inflammatory, antioxidant, anti-tumor, and immunomodulatory effects16. In immune-related diseases, GE inhibits overactive T and B cell responses, reduces autoantibody production, and alleviates immune-mediated tissue damage43. Additionally, GE improves the imbalance between innate and adaptive immunity by regulating immune cell interactions44. GE’s immunomodulatory effects include regulating macrophage polarization. It inhibits pro-inflammatory M1 macrophages while enhancing the function of anti-inflammatory M2 macrophages, promoting tissue repair and reducing chronic inflammation45,46.GE effectively inhibits inflammatory responses by regulating the JAK-STAT signaling pathway, indicating its potential as an immunomodulator. In CIA models, GE shows good curative effect, which can improve arthritis symptoms and reduce joint injury. These results provide preliminary evidence for clinical trials, systematic clinical evaluation and research can provide scientific basis for its application in RA treatment and promote the development of related fields. These findings provide a theoretical basis for the development of new immunotherapy for rheumatoid arthritis and emphasize the importance of targeting specific immune pathways for curative effects. Therefore, this study offers valuable insights for understanding the immune mechanisms of RA and paves the way for exploring the broader immunomodulatory effects of GE in the future.
Methods
Prediction of GE putative targets
The TCMSP network data platform (https://tcmsp-e.com/tcmsp.php)47 was used to search for targets related to’Geniposide’. Targets of GE were further screened by using the SwissTargetPrediction (http://www.swisstargetprediction.ch)48, Pharmmapper (http://www.lilab-ecust.cn/pharmmapper)49, and Batman (http://bionet.ncpsb.org.cn/batman-tcm)50 databases. The acquired target proteins were normalized by using the UniProt database. Next, any duplicate targets were removed to obtain potential candidates used for further analysis.
Collection of RA-related targets
Gene expression data for RA and healthy synovial tissue (GSE55235) were obtained from the National Center for Gene Expression Comprehensive dataset (https://www.ncbi.nlm.nih.gov/geo). Synovial tissue samples from 10 RA patients and 10 healthy donors were selected within the GSE55235 dataset. Using R language (version 4.2.1, https://www.xiantaozi.com/) and the ComplexHeatmap package (version 2.13.1), The differentially expressed genes in these samples were analyzed. The results were screened by using a p-value threshold of <0.05 and |logFC|≥1 to identify potential targets for RA.
Drug-disease intersection targets and PPI network construction
The drug targets and potential targets of RA were analyzed by using Venny 2.1.0 to identify potential targets of GE against RA. Subsequently, expression heat maps of these therapeutic targets were generated using GraphPad Prism 9.0 software. The identified targets were uploaded to the STRING database (https://string-db.org/)51 to construct a protein-protein interaction (PPI) network. The organism was specified as Homo sapiens, and a minimum interaction score of > 0.4 was set. The PPI network, saved in TSV format, was imported into Cytoscape 3.9.0 to analyze protein interaction relationships and construct the network map.
Screening key targets
A topological analysis of the network graph was conducted by using the CytoNCA plug-in in Cytoscape 3.9.0. The screening parameters used were Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), and Network Centrality (NC). DC reflects the importance of connection, which is easy to calculate and understand. BC reflects the criticality of information transmission and identifies key control points. CC measures global influence and supplements centrality information. NC considers neighboring influence and mines potential key nodes. These four central attribute parameters assess the importance of network nodes; a higher value indicates that the node is closer to the network’s center. Consequently, the top 20 intersecting targets were identified as key targets of GE in relation to RA.
GO and KEGG analysis
Gene Ontology (GO) analysis and KEGG pathway analysis52,53 were conducted for the GE-RA intersection targets using R version 3.6.3 along with the associated R packages: clusterProfiler, Org.Hs.eg.db, and ggplot2. A p-value of less than 0.01 was deemed statistically significant after adjustment using the Benjamini-Hochberg (BH) method.
RA clinical diagnostic value of the ROC curve analysis for the key genes
The analysis of ROC (Receiver Operating Characteristic) curves for key genes is essential for assessing their diagnostic value in clinical studies. ROC curves assist researchers in determining how effectively genes can differentiate between patient groups (e.g., disease group) and control groups (e.g., healthy group). The expression profiles of 12 key genes from the GSE55235 dataset were downloaded and imported into the Xiantao Academic ROC calculation section. The predictive ability of the core target diagnosis was then evaluated using the“pROC”package. The AUC (Area Under the Curve) of the ROC is calculated to assess the prediction model’s accuracy; a higher AUC indicates greater accuracy and clinical diagnostic value for the corresponding gene.
Molecular docking
Molecular docking assays were used to study how drugs bind to key targets. We obtained the 3D structures of 12 RA-related key targets from the Protein Data Bank (PDB, https://www.rcsb.org/) and downloaded them in mol2 format. We preprocessed the protein structure using AutoDock software (Version 1.5.7, https://ccsb.scripps.edu/mgltools/) by removing water, adding polar hydrogen, calculating Gasteiger charges, and adjusting the grid box size. Hydrogen atoms, Gasteiger charges, and rotatable bonds were assigned to the ligand GE. Molecular docking was performed using AutoDock Vina (The Scripps Research Institute, 4.2.6), and binding energy values were calculated. A lower binding score indicates a stronger binding effect: scores below −4.25 kcal/mol suggest binding activity between the ligand and target, scores below −5.0 kcal/mol indicate good binding activity, and scores below −7.0 kcal/mol indicate strong docking activity54. Finally, we utilized the PyMOL program were utilized to visualize the binding map from the molecular docking.
Cell culture and treatment
The RA-FLS cell line was obtained from Genio Biotechnology, located in Guangzhou. RA-FLS cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) with 10 % fetal bovine serum (Gibco, USA) and supplemented with 1 % penicillin-streptomycin (Hyclone, USA). The compound GE, identified as TTL331 with a purity of 98 % by HPLC, was purchased from Shanghai Ronghe Pharmaceutical Technology Development Co, Ltd.
MTT assay
Cell proliferation was measured by using the MTT assay (Biosharp, BL132B). RA-FLS cells were seeded in 96-well plates at a density of 5×103 cells per well and incubated at 37 °C in a 5 % CO2 incubator. Afterward, the cells were pretreated with 0, 25, 50, 100, or 200 μM GE. They were then incubated with TNF-α for either 24 or 48 hours. The volume of 20 μL of MTT solution (5 mg/mL, 0.5 % MTT) were added to each well and the culture continued for 4 h. After centrifugation, the supernatant was discarded and 150 μL of DMSO were added to each well to dissolve the formazan crystals. The absorbance at 490 nm was measured using a microplate reader. Relative values of cell viability were calculated as a percentage of the untreated controls.
Enzyme-Linked Immunosorbent Assay (ELISA)
RA-FLS cells in the logarithmic growth phase were seeded at a density of 1×105/mL per well in 6-well plates. After overnight incubation, the cells were pre-treated with 0, 25, 50, and 100 μM GE in the 6-well plates, followed by a 24-hour incubation with TNF-α. The supernatants from the treated cells in each group were collected. These were then tested by using an ELISA kit (Shanghai Enzyme Link) for TNF-α, IL-8, IL-17, MMP-3, and MMP-9. The absorbance of each well was measured at specific wavelengths by using a microplate reader. The data were processed with Origin software, and the experiments were repeated in triplicate.
Quantitative real-time polymerase chain reaction (RT-qPCR)
RA-FLS cells were seeded in 6-well plates at 5×105 cells/well and incubated with TNF-α for 48 h. Cells were harvested by trypsinization. Total RNA from RA-FLS cells was extracted using a fast-type Cell RNA extraction kit (AG21023, Accurate Biology). The cells were washed twice with PBS, and 500 μL of lysate Buffer RLS was then added to the EP tube. There was no obvious precipitation in the EP tube, and it was allowed to stand at room temperature for 2 minutes. Purification: An equal volume of 70 % ethanol and lysate was added to the EP tube and mixed well. Next, 600 μL of Buffer RWA was added, followed by centrifugation of 650 μL of Buffer RWB for 1 minute at room temperature. The adsorption column of the Mini Column was removed and placed on an RNase Free Tube, and 100 μL of RNase Free Water was slowly added to the adsorption column. The column was left at room temperature for 5 minutes and then centrifuged for 2 minutes. The extracted RNA was subsequently stored at −80 ℃. The cDNA was synthesized using a reverse transcription reagent (RK20428, ABclonal). A total amount of 1 µg of RNA was added, and the corresponding volume was calculated and transferred to the PCR tube. Then, 4 μL of 5×ABScript III RT Mix was added, and nuclease-free H2O was added to reach a final volume of 20 µL. The mixture was briefly centrifuged and placed into a PCR instrument to synthesize cDNA. The qPCR was performed using SYBR Green Fast qPCR Mix (RK21205, ABclonal), with β-actin set as the internal reference. Finally, the relative amount of mRNA was calculated using the 2−∆∆Ct method.
Western blotting (WB)
RA-FLS cells were cultured in T25 flasks and treated with TNF-α for 48 h. After treatment, the cells were lysed by using 1×RIPA lysis buffer containing 1 % PMSF and 1 % phosphotransferase inhibitor. Protein concentration was determined by using the BCA protein quantification kit (Thermo Fisher Scientific, USA). Subsequently, the protein (25μg) was denatured by heating and then electrophoresed by 10 % SDS-PAGE onto a polyvinylidene difluoride membrane (Millipore, USA). The membrane was blocked with 5 % skim milk (or 5 % bovine serum albumin) for 1 hour at 37 ℃. It was then incubated overnight at 4 ℃ with primary antibodies, including JAK1 (66466-1-lg, Proteintech), p-JAK1 (bs-3238R, Biss), STAT1 (66545-1-lg, Proteintech), p-STAT1 (28979-1-AP, Proteintech), and β-actin (AF7018, Affinity). After incubation, the membrane was incubated with the secondary antibody bound to horseradish peroxidase (HRP) (SA00001-1, SA00001-2, Proteintech) for 1.5 h at 37 ℃. Finally, specific bands were detected using an enhanced chemiluminescence reagent (Thermo Fisher Scientific) and quantified using Image J software.
In vivo experiment
CIA model was induced by 7-week-old male C57BL/6 mice. Mice were purchased from Chengdu Yao Kang Biotechnology Co., Ltd. (Chengdu, China). One week before the experiment, all mice adapted to the laboratory environment (20 ± 2 C; 60 ± 5% RH; 12 ± 12 hours light/dark cycle). All experimental procedures were approved by the Ethics Committee of Animal Room of Chengdu Medical College (license number: SCXK, 2024–077). We confirmed that all experiments in this study were performed in accordance with the relevant guidelines for the care and use of experimental animals. All experiments were performed according to the ARRIVE Guidelines Checklist for animal in vivo experiments. The CIA mouse model is based on the following steps. On the 0 th day, the chicken type II collagen was emulsified with the same amount of complete Freund’s adjuvant (CFA). Each mouse was injected with 200 μl of emulsion at the caudal base. On the 21 st day, chicken type II collagen was emulsified with the same amount of incomplete Freund’s adjuvant (IFA) and injected into the root of mouse tail in the same way as on the 0th day. After successful modeling, a total of 50 mice were divided into five groups: negative control group, CIA group, positive control group, low-dose GE group and high-dose GE group. The negative control group and CIA group were given normal saline, and the low-dose group was given GE 60 mg/kg/d (GE-L). The high-dose group was given GE 120 mg/kg/d (GE-H), and the positive group was given 0.5 mg/kg/2d MTX. On the 45th day, all mice were killed by 4 % isoflurane anesthesia and cervical dislocation. Before killing these mice, two researchers measured the weight and thickness of each foot pad every 3 days. Peripheral blood was collected, centrifuged at 4 ℃ and 3000 rpm for 10 min, and serum was taken. Serum cytokines of mice were detected by mouse inflammation kit, and the content of cytokines in serum of mice was detected by ELISA kit. The joints of mice were stained with HE, and the effect of GE treatment on the joints was observed.
Statistical analysis
Data are expressed as the mean ± standard deviation (SD), and all statistical analyses were conducted using GraphPad Prism version 9.0. Data were analyzed using one-way and two-way ANOVA, with P < 0.05 indicating statistical significance. Each experiment was conducted in triplicate to ensure reliability.
Conclusions
In conclusion, our study shows that GE could be a promising therapeutic agent for RA. We identified 53 common targets and highlighted key pathways like JAK-STAT. Our findings demonstrate that GE can inhibit the proliferation of RA-FLS cells and lower the levels of pro-inflammatory cytokines in vitro and in vivo. These findings indicate that GE could provide a new way to treat RA, improving on the limitations of existing therapies. Future research should prioritize in vivo studies and clinical trials to confirm these findings and fully explore GE’s therapeutic potential in managing RA.
Author contributions
Meng Huang, Jing Jiang and Yue-Jia Li conducted most of the experiments together, analyzed the processing results, drew diagrams, and prepared the first draft of the manuscript; Meng-Ying Jiang, Min Yang and Xiao Leng participated in some experimental preparations; Hui Deng, Yong-Gang Wu and Li-Juan Wu helped to prepare the manuscript and discuss the project; Jian-Lin Chen and Wen-Kui Sun supervised the study and wrote the final version of the revised manuscript. All authors have read and agree to the published version of the manuscript.
Funding
This research was supported by several research grants, including the National Natural Science Foundation of China (31701104), the Sichuan Provincial Administration of Traditional Chinese Medicine (2024MS519, 2024MS010), the Open Fund of Development and Regeneration Key Laboratory of Sichuan Province (24FYYZS13), the Collaborative Fund of Chengdu Medical College – People’s Hospital of Xindu District in Chengdu (2022LHXD-03), and the 2024 Provincial Student Innovation and Entrepreneurship Project of Chengdu Medical College (S202413705040).
Data availability
Data is provided within the manuscript and supplementary information files.
Declarations
Competing interests
The authors declare no competing interests.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1038/s41598-025-10196-7.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Abstract
Rheumatoid arthritis (RA) is a chronic autoimmune disease that affects millions worldwide, characterized by joint pain, swelling, and functional impairment. Current treatments like Non-steroidal anti-inflammatory drugs (NSAIDs), corticosteroids, and biologics, have limitations including side effects and resistance problems, which create a need for new therapeutic strategies. This study aims to explore the potential therapeutic role and mechanisms of Geniposide (GE), a natural compound extracted from Gardenia jasminoides, in RA treatment. Through network pharmacology methods and using target prediction databases including TCMSP, SwissTargetPrediction, Pharmmapper, and Batman, 330 potential targets of GE were identified. In RA, 1324 differentially expressed genes (DEGs) were identified from the GSE55235 dataset. By intersecting the datasets, 53 shared targets were identified, which were further analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, with pathways like IL-17 and JAK-STAT being significantly highlighted. Additionally, protein–protein interaction (PPI) network analysis identified 12 key targets (EGFR, MMP-9, CCL5, PPARG, STAT1, HCK, SYK, MAPK8, CTSB, RAC2, JAK2, TYMS) with high degree values. Furthermore, molecular docking studies confirmed strong binding affinities between GE and the identified targets. Experimental validation demonstrated that GE inhibited RA-FLS cell proliferation in a dose-dependent manner by using MTT assays and reduced the level of pro-inflammatory cytokines (IL-17, IL-8, TNF-α, MMP-3, MMP-9) as measured by ELISA. RT-qPCR and Western blot analyses further confirmed that GE modulated the mRNA expression of key targets and inhibited the phosphorylation of JAK1 and STAT1 proteins, respectively. Finally, we verified the anti-inflammatory effect of GE on CIA mice through in vivo experiments. These findings suggest that GE has anti-RA effects by targeting several key molecules and pathways. This provides a theoretical basis for developing GE as a novel therapeutic for RA.
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Details
1 Chengdu Medical College, School of Laboratory Medicine, Chengdu, China (GRID:grid.413856.d) (ISNI:0000 0004 1799 3643)
2 Sichuan Taikang Hospital, Department of Clinical Laboratory, Chengdu, China (GRID:grid.413856.d)
3 Xindu District People’s Hospital, Department of Orthopedics, Chengdu, China (GRID:grid.413856.d)
4 Chengdu Medical College, Department of Library, Chengdu, China (GRID:grid.413856.d) (ISNI:0000 0004 1799 3643)
5 Center for Scientific Research of Chengdu Medical College, Chengdu, China (GRID:grid.413856.d) (ISNI:0000 0004 1799 3643)
6 Chengdu Medical College, School of Laboratory Medicine, Chengdu, China (GRID:grid.413856.d) (ISNI:0000 0004 1799 3643); Chengdu Medical College, Development and Regeneration Key Laboratory of Sichuan Province, Chengdu, China (GRID:grid.413856.d) (ISNI:0000 0004 1799 3643)