Abstract Rheumatoid arthritis (RA) is a chronic, systemic, autoimmune disease that may lead to joint damage, deformity, and disability, if not treated effectively. Hedyotis diffusa Willd (HDW) and its main components have been widely used to treat a variety of tumors and inflammatory diseases. The present study utilized a network pharmacology approach, microarray data analysis and molecular docking to predict the key active ingredients and mechanisms of HDW against RA. Eleven active ingredients in HDW and 180 potential anti-RA targets were identified. The ingredients-targets-RA network showed that stigmasterol, beta-sitosterol, quercetin, kaempferol, and 2-methoxy-3-methyl-9,10-anthraquinone were key components for RA treatment. KEGG pathway results revealed that the 180 potential targets were inflammatory-related pathways with predominant enrichment of the AGE-RAGE, TNF, IL17, and PI3K-Akt signaling pathways. Screened through the PPI network and with Cytoscape software, RELA, TNF, IL6, TP53, MAPK1, AKT1, IL10, and ESR1 were identified as the hub targets in the HDW for RA treatment. Molecular docking was used to identify the binding of 5 key components and the 8 related-RA hub targets. Moreover, the results of network pharmacology were verified by vitro experiments. HDW inhibits cell proliferation in MH7A cells in a dose and time-dependent manner. RT-qPCR and WB results suggest that HDW may affect hub targets through PI3K/AKT signaling pathway, thereby exerting anti-RA effect. This study provides evidence for a clinical effect of HDW on RA and a research basis for further investigation into the active ingredients and mechanisms of HDW against RA. Subject terms: Biochemistry, Health occupations, Rheumatology Introduction Rheumatoid arthritis (RA), one of the most common autoimmune diseases, is characterized by invasive joint synovitis, pannus formation, deterioration of joints, and loss of joint function^[34]1,[35]2. RA occurs in 0.5 to 1.0% of the population worldwide^[36]3. In China, up to 5 million people suffer from pain and recurrence of RA^[37]4. Due to its high prevalence, debilitating nature, and disabling consequences, RA has generated a substantial clinical, economic, and social burden. The pathogenesis of RA is complicated, incompletely understood, and considered to be mediated by various mechanisms. At present, the typical events in the pathogenesis of RA are considered to be hyperplasia of cells in the synovial membrane consisting of synovial fibroblasts, macrophages, and lymphocytes^[38]5. Some studies have shown that fibroblast-like synoviocytes (FLS) are the dominant cell type and are considered to play a critical role in the pathogenesis of RA^[39]6,[40]7. Disease-modifying antirheumatic drugs (DMARDs) and non-steroidal anti-inflammatory medications (NSAIDs) have been widely used in RA therapy^[41]8. Conventional DMARDs, including hydroxychloroquine, methotrexate (MTX), sulfasalazine, and leflunomide, have been approved by the US Food and Drug Administration (FDA) as a first-line therapy for RA patients^[42]9; however, there are no truly effective pharmacotherapies for the treatment of RA. Most of these drugs have frequent side effects, including gastrointestinal irritation, kidney injury, and cardiovascular risk^[43]10. There is therefore an urgent need for safe and effective medical treatments for RA. In recent years, with the understanding of pathogenesis of RA in Chinese medicine, some progress has been made in the treatment of RA with traditional Chinese medicine. Hedyotis Diffusa Willd (HDW) is a member of the Rubiaceae family of Chinese herbal remedies and is mainly found in the southeastern provinces of China^[44]11. Modern pharmacological studies have shown that HDW exhibits multiple pharmacological effects, including anti-tumor, anti-inflammatory, anti-oxidation, anti-fibroblastic, hepatoprotective and immunomodulatory^[45]12,[46]13. It has been widely studied as a potential therapeutic drug for treatment of malignant tumors of the breast, stomach, colon, rectum, cervix, and ovary^[47]14–[48]16. It has also been used in the treatment of inflammation-related diseases, including urinary tract infection, colitis, tonsillitis, appendicitis, pharyngitis, hepatitis, dysentery, diarrhea, and snake bites^[49]17,[50]18. Studies have shown that the certain chemical constituents of HDW (scandoside, asperuloside and asperulosidic acid) exerted an anti-inflammatory effect on LPS-induced RAW 264.7 macrophages by suppressing the NF-κB and MAPK signaling pathways^[51]19,[52]20. In a complete Freund's adjuvant (CFA)-induced arthritis model in rats, 12 days of oral treatment with HDW extract ursolic acid (50 mg/kg/day) was demonstrated to suppress paw swelling, plasma PGE (2) production, spinal Fos expression, and arthritis-induced mechanical and thermal hyperalgesia^[53]21. Zhu et al.^[54]22,[55]23 also revealed that HDW compounds ferulic acid and p-coumaric acid demonstrated an anti-inflammatory effect on collagen-induced arthritis as indicated by decreased numbers of inflammatory cells and reduced levels of IL-1β and TNF-α. Intriguingly, unfractionated HDW had a better therapeutic outcome than ferulic acid, although a poorer one than p-coumaric acid alone. A recent study has shown that HDW effectively suppressed the progression of disease in a collagen-induced arthritis (CIA) model by reducing the arthritis index, by reducing levels of IL-lβ, TNF-α, PGE2, RANKL, OPG, and RANKL/OPG, and by increasing the pain threshold^[56]24. Nevertheless, despite extensive studies on the pharmacological effect of HDW, the potential targets and underlying molecular mechanism(s) of HDW in RA remain unclear. Network pharmacology is a novel approach that combines system network analysis and pharmacology^[57]25. Through network pharmacological analyses, we can investigate TCM systematically, identify the active components, predict potential targets and mechanisms, and provide the opportunity for modernization of TCM^[58]26. Therefore, our study aimed to investigate the active ingredients, potential targets, and the underlying mechanism of HDW for the treatment of RA by adopting a network pharmacology approach, molecular docking and cell experiments. The main scheme of this study is presented in Fig. [59]1. We screened public databases (TCMSP) and published literature to identify the active ingredients of HDW. Then, network pharmacology was used to analyze ingredients targets, drug targets, biological processes and pathways in RA treatment. In addition, we also used cell experiments to identify the results of network pharmacology. Figure 1. [60]Figure 1 [61]Open in a new tab A flow-chart of this study to investigate the potential mechanism of HDW in treatment of RA. Results Active components and potential targets of HDW Our study performed network pharmacology prediction based on network pharmacology evaluation method guidance‐Draft^[62]27. A total of 142 related components of HDW was retrieved from TCMSP and the published literature. According to pharmacokinetic characteristics (OB ≥ 30% and DL ≥ 0.18) and ADME information, 11 active components were selected from 142 ingredients of HDW. The TCMSP and Swiss Target Prediction databases were used to determine the pharmacological targets of the HDW components. Table [63]1 shows active components and the number of the corresponding potential targets of HDW. Detailed information of these components and targets is listed in Supplementary Table [64]1. Eventually, 180 potential targets were identified (after removing duplicates) using the Uniprot database. Table 1. Active components and numbers of corresponding potential HDW targets. PubChem CID Active components Target number 5281330 Poriferasterol 2 10514946 2-Methoxy-3-methyl-9,10-anthraquinone 31 5280794 Stigmasterol 31 222284 β-Sitosterol 38 5280343 Quercetin 154 5280863 Kaempferol 17 5280460 Scopoletin 3 637542 p-Coumaric acid 13 72 3, 4-Dihydroxybenzoic acid 8 445858 Ferulic acid 8 135 p-Hydroxybenzoic acid 12 [65]Open in a new tab Identification of the potential targets of RA Using “Rheumatoid Arthritis” as the search term, 42, 192, 141, 174, and 623 disease-targets were obtained from the OMIM, DrugBank, TTD, GeneCards, and DisGeNET databases, respectively. Merging all results from the five databases and removing duplicates, 942 related-RA potential targets were finally collected. Construction of the active components-common targets-RA network Applying a Venn diagram, 85 common targets were found overlapped between HDW compound targets and RA-related targets (Fig. [66]2A). We imported 11 active components and 85 common targets into Cytoscape 3.9.0 software to construct a components-targets-RA network. Among these, the active ingredients with the highest degree value were stigmasterol, β-sitosterol, quercetin, kaempferol and 2-methoxy-3-methyl-9,10-anthraquinone. However, poriferasterol and scopoletin were removed since they lacked common targets in the network. The active ingredients-targets-RA network is shown in Fig. [67]2B. Results indicate that 5 components may provide the key to successful treatment of RA. Figure 2. [68]Figure 2 [69]Open in a new tab Construction of the components-targets-RA Network. (A) Venn diagram of active ingredients of HDW and RA targets. (B) Active ingredients-targets-RA network. Purple triangles represent 9 components of HDW and yellow rectangles represent common targets. GO and KEGG enrichment analysis In total, GO analysis identified 1542 significantly enriched GO terms (P.adjusted < 0.01 adjusted with Benjamini–Hochberg), consisting mainly of 1459 biological processes, 18 cellular components, and 65 molecular functions. We screened the top 10 ranked GO terms shown in Fig. [70]3A. In the biological process (GO:BP) category, the top terms were involved in responses to lipopolysaccharides, molecules of bacterial origin, and reactive oxygen species metabolic processes. In the cellular component (GO:CC) category, the top terms included membrane rafts, membrane microdomains, and membrane regions. In the molecular function (GO:MF) category, the top terms consisted of nuclear receptor activity, transcription factor activity, and cytokine receptor binding. To further identify underlying signaling pathways, we analyzed KEGG pathways. The top 20 significantly enriched pathways (P.adjusted value < 0.01) are shown in Fig. [71]3B. A list of genes contributing to the 20 selected pathways is provided in Supplementary Table [72]2. Numerous targets were found associated with the AGE-RAGE, TNF, IL17, and PI3K-Akt signaling pathways, all of which are associated with the prognosis and onset of RA. Figure 3. [73]Figure 3 [74]Open in a new tab GO and KEGG analysis of potential targets. (A) Different colors represent different categories. The height of the column represents the P.adjust value: the higher the value, the higher the reliability of the GO categories (P.adjust < 0.01). (B) Dot size indicates the number of target genes in the pathway, and dot color reflects the different P.adjsted value ranges. Network visualization and identification of hub targets Next, we analyzed 85 potential therapeutic targets by using the STRING database to obtain a PPI network to explore the relationship between RA-related targets. The PPI relationship network, with a total of 85 nodes, 238 edges and an average node degree of 5.6 was generated with a confidence of 0.9 (Supplementary Fig. [75]1). PPI network diagrams were imported into Cytoscape 3.9.0 software for visualization (Fig. [76]4A). We further identified the subnetwork and hub targets from the PPI network using the CytoNCA plug-in (Fig. [77]4B). As shown in Fig. [78]4C, a subnetwork was identified, including 8 nodes and 27 edges. Moreover, RELA, TNF, IL6, TP53, MAPK1, AKT1, IL10, and ESR1 were identified as the hub targets in the HDW for RA treatment (Supplementary Table [79]3). Figure 4. [80]Figure 4 [81]Open in a new tab Construction of the PPI network and screening hub targets. (A) PPI network of 85 common targets. (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 8 hub targets. The color and size of the nodes reflect the degree value for each protein target: the larger and darker the node, the greater the degree value. The different colored lines in the figure represent known interactions and predicted interactions (light blue: from curated databases; dark purple: experimentally determined; green: gene neighborhood; red: gene fusions; dark blue: gene co-occurrence; yellow: textmining; black: co-expression; light purple: protein homology). Molecular docking Candidate compounds stigmasterol, β-sitosterol, quercetin and kaempferol, and 2-methoxy-3-methyl-9,10-anthraquinone, are the top 5 (ranked by degree) in the compounds-targets-RA network. The hub targets, RELA, TNF, IL6, TP53, MAPK1, AKT1, IL10, and ESR1, play a significant role in the action of HDW against RA. Molecular docking of the 5 compounds and 8 hub genes revealed binding energies shown in Fig. [82]5. Five components of HDW exhibited strong binding to the 8 core targets with β-sitosterol showing the highest binding energy. these results imply that treatment with HDW may affect all the Figure targets in RA patients. The target proteins and the small molecules with strong binding affinity were visualized by PyMoL software (Fig. [83]6). Figure 5. Figure 5 [84]Open in a new tab Molecular docking heatmap of the main compounds and key targets (kcal/mol). The figure shows the size of the binding energy. The larger the absolute value, the redder the color, indicating increasing stability of the combination of the component and the target protein. Figure 6. [85]Figure 6 [86]Open in a new tab Docking complexes of ligand and receptor proteins and their binding residues are shown using PYMOL software. (A) RELA and β-sitosterol. (B) TNF and quercetin. (C) IL6 and quercetin. (D) TP53 and stigmasterol. (E) MAPK1 and β-sitosterol. (F) AKT1 and quercetin. (G) IL10 and β-sitosterol. (H) ESR1 and β-sitosterol. Cell experiments Overproliferation of fibroblast-like synoviocytes (FLS) are important pathogenesis of RA. Therefore, we investigated the effect of HDW at different concentrations (0, 0.5, 1, 2 mg/mL) on the proliferation of MH7A cells after 48 h. CCK-8 assay showed that HDW inhibited the proliferation of MH7A cells in a dose-dependent (Fig. [87]7A). According to the results, we chose to perform subsequent experiments with the dose of 0.5, 1, and 2 mg/mL. The WB result showed that HDW could inhibit the PI3K/AKT pathway by reducing phosphorylation of AKT in MH7A cells (P < 0.05, Fig. [88]7B). Hub targets in our work were verified by RT-qPCR and results showed that RELA, TNF, and IL6 were up-regulated while IL10 was down-regulated (Fig. [89]7C). These results validated our network pharmacology analysis, suggesting that HDW can play a role in treating RA by regulating PI3K/AKT signaling pathway and RA-related targets. Figure 7. [90]Figure 7 [91]Open in a new tab Cell experiments validate results of network pharmacology. (A) CCK8 assays of different HDW concentrations (0, 0.5, 1, 2 mg/mL) incubated MH7A cells for 48 h. (B) The expression levels of AKT and p-AKT were measured using western blotting. Original blots are presented in Supplementary Fig. [92]2. (C) The effect of HDW on the mRNA levels of RELA in MH7A cells. Compared with control (0 mg/mL), *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Discussion Given their potent anti-inflammatory, anti-fibroblastic, and immunomodulatory actions, HDW and its components have been widely used in animal models over the last several years as an intervention for RA^[93]12,[94]13,[95]19–[96]23. Unfortunately, the potential targets and molecular mechanism of HDW against RA are still inadequately understood. TCM network pharmacology emerging recently has become a flourishing field in TCM modern studies along with the rapid progress of bioinformatics. The study of TCM and biological network appeared for the first time in 2007^[97]28, and some applications of traditional medicine network pharmacology for herbs or herbal formulae in RA^[98]29. In the present network pharmacological analysis, a total of 142 compounds of HDW were identified from TCMSP and published literature, and 11 compounds were selected by TCMSP and ADME criteria screening. A total of 180 targets related to potential compounds and 942 targets associated with RA were identified, and 85 common target genes were obtained from the overlapping part of identified compounds and RA. The components-targets-RA network analysis visualized the interaction of multi-components and multitargets about HDW on RA. The compounds targets network analysis indicated that the 5 compounds, including stigmasterol, β-sitosterol, quercetin, kaempferol, and 2-methoxy-3-methyl-9,10-anthraquinone, were linked to ≥ 10 target genes, and the 8 target genes (RELA, TNF, IL6, TP53, MAPK1, AKT1, IL10, and ESR1) were core target genes in the network. GO enrichment analysis indicated that numerous targets are involved in response to lipopolysaccharides and molecules of bacterial origin in BP, are localized to membrane rafts and membrane microdomains in CC, and are associated with nuclear receptor and transcription factor activities in MF. KEGG pathway analysis indicated that numerous targets are associated with certain inflammatory events and cancer. Molecular docking showed that stigmasterol, β-sitosterol, quercetin, kaempferol, and 2-methoxy-3-methyl-9,10-anthraquinone have good binding activity with RELA, TNF, IL6, TP53, MAPK1, AKT1, IL10, and ESR1 targets. Finally, the molecular mechanisms of HDW predicted by network pharmacology approach against RA were validated by in vitro experiments. A potential components-targets-RA target network indicated that stigmasterol, β-sitosterol, quercetin, kaempferol, and 2-methoxy-3-methyl-9,10-anthraquinone, are likely to play vital roles in the process of RA treatment (Table [99]2). Indeed, apart from 2-methoxy-3-methyl-9,10-anthraquinone, all these components have previously been reported to exhibit potential antirheumatic therapeutic activity. For example, stigmasterol has been shown to protect CIA rats by suppressing proinflammatory mediators (TNF-α, IL-6, IL-1β, iNOS and COX-2) and increasing anti-inflammatory cytokine IL-10^[100]30. β-sitosterol exerts an inhibitory influence on synovial angiogenesis by suppressing endothelial cell proliferation and migration, thereby alleviating joint swelling and bone destruction in CIA mice^[101]31, and quercetin inhibits the release of proinflammatory cytokines (IL-6, TNF-α, IL-1β, IL-8, IL-13, IL-17) by activating SIRT1, thereby becoming a potential effector of RA^[102]32,[103]33. Kaempferol inhibits the proliferation and migration of RA-FLSs and the release of activated T-cell-mediated inflammatory cytokines by suppressing fibroblast growth factor receptor 3-ribosomal S6 kinase 2 (FGFR3-RSK2) signaling^[104]34. Collectively, these active components exhibit antirheumatic activity by various mechanisms, including anti-inflammatory, immunoregulatory, and reduction of bone destruction. Notably, however, there have been few previous studies on the treatment of RA with stigmasterol and β-sitosterol. Table 2. Potential anti-RA mechanisms of some compounds. Compound Mechanism Model References