Network Pharmacology and Bioinformatics Identify the Potential Mechanism Underlying the Use of Hedyotis diffusa Willd for Treatment of Rheumatoid Arthritis

doi:10.21203/rs.3.rs-1632853/v1

Download PDF

Article

Network Pharmacology and Bioinformatics Identify the Potential Mechanism Underlying the Use of Hedyotis diffusa Willd for Treatment of Rheumatoid Arthritis

https://doi.org/10.21203/rs.3.rs-1632853/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 25 Jan, 2023

Read the published version in Scientific Reports →

You are reading this latest preprint version

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, the expression levels of 8 hub targets, RELA, TNF, IL6, TP53, MAPK1, AKT1, IL10, and ESR1, were determined using 3 Gene Expression Omnibus (GEO) datasets and The Human Protein Atlas (HPA). Molecular docking was used to identify the binding of 5 key components and the 8 related-RA hub targets, providing 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.

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 ^1,2. RA occurs in 0.5 to 1.0% of the population worldwide ³. In China, up to 5 million people suffer from pain and recurrence of RA 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 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 ^6,7. Disease-modifying antirheumatic drugs (DMARDs) and non-steroidal anti-inflammatory medications (NSAIDs) have been widely used in RA therapy 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 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 ¹⁰. There is therefore an urgent need for safe and effective medical treatments for RA.

Over the past thousands of years, Traditional Chinese Herbal Medicines (TCHMs) have been used for the treatment and prevention of a wide range of medical ailments and diseases, and the effects of herbal medicineare attracting increasing attention ^11,12. 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 ¹³. HDW has the effect of heat cleaning and detoxicating, pain-relieving and dispelling masses, diuresis, and dehumidification. Modern pharmacological studies have shown that HDW exhibits multiple pharmacological effects, including anti-tumor, anti-inflammatory, anti-oxidation, anti-fibroblastic, hepatoprotective and immunomodulatory ^14,15. It has been widely studied as a potential therapeutic drug for treatment of malignant tumors of the breast, stomach, colon, rectum, cervix, and ovary ^16–18. 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 ^19,20. 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 ^21,22. In a complete Freund's adjuvant (CFA)-induced arthritis model in rats, 12 days of oral treatment with HDW extract ursolic acid (50mg/kg/day) was demonstrated to suppress paw swelling, plasma PGE(2) production, spinal Fos expression, and arthritis-induced mechanical and thermal hyperalgesia ²³. Zhu et al, ^24,25 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 ²⁶. 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 new discipline combining systems biology with drug efficacy, which helps elucidate the inherent multi-component, multi-target, and multi-pathway characteristics of Traditional Chinese Medicine (TCM) ²⁷. 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 ²⁸. 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, with microarray data analysis and molecular docking. The main scheme of this study is presented in Fig. 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 Gene Expression Omnibus (GEO) and The Human Protein Atlas (HPA) to identify the expression ofcrucial targets.

Active Components and Potential Targets of HDW. 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 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 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

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 (Figure 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 Figure 2B. Results indicate that 5 components may provide the key to successful treatment of RA.

GO and KEGG Enrichment Analysis. In total, GO analysis identified 1,542 significantly enriched GO terms (P.adjusted < 0.01 adjusted with Benjamini-Hochberg), consisting mainly of 1,459 biological processes, 18 cellular components, and 65 molecular functions. We screened the top 10 ranked GO terms shown in Figure 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 Figure 3B. A list of genes contributing to the 20 selected pathways is provided in Supplementary Table 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.

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 Figure 1). PPI network diagrams were imported into Cytoscape 3.9.0 software for visualization (Figure 4A). We further identified the subnetwork and hub targets from the PPI network using the CytoNCA plug-in (Figure 4B) . As shown in Figure 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 3).

Validation of the Expression of the Hub Targets. To further validate the expression of 8 hub genes in RA, 3 gene expression microarray data sets were downloaded from the GEO database. Based on these, we obtained a normalized expression of hub genes in synovial cells, CD4⁺T cells and macrophages, as shown in Figure 5. Of the targets, RELA, TNF, IL6, MAPK1, and IL10 were significantly associated with key cells of RA pathogenic mechanism (FLS, CD4⁺T cells, and macrophages). To further analyze the functions of hub targets in RA in different cell types, we determined the expression of 8 hub targets in the HPA database (Figures 6 and Supplementary Figure 2). The results indicated that TNF and IL10 had high immune cell specificity, while IL6 was specifically expressed in fibroblast-like synoviocytes.

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 Figure 7. 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 (Figure 8).

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 ^{14,15,21−25}. Unfortunately, the potential targets and molecular mechanism of HDW against RA are still inadequately understood. 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 compoundstargets 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.

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 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 ²⁹. 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 ³⁰, 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 ^31,32. 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 ³³. 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.

Construction of the common target-related PPI network permitted screening of the top 8 core genes: RELA (transcription factor p65), TNF (tumor necrosis factor), IL6 (interleukin-6), TP53 (cellular tumor antigen p53), MAPK1 (mitogen-activated protein kinase 1), AKT1(RAC-α serine/threonine-protein kinase), IL10 (interleukin-10), and ESR1(estrogen receptor). RELA, ranking first with the highest connection in the PPI network, is a subunit of nuclear factor (NF)-κB and is regarded as an important member of the NF-κB pathway ³⁴. RELA participates in essential life activities, including cell proliferation, transformation, ap-

Table 2

Potential anti-RA mechanisms of some compounds.
Compound	Mechanism	Model	Reference
stigmasterol	Suppressed proinflammatory mediators and increased anti-inflammatory cytokine through down-regulating NF-kB, p65 and p38MAPK.	CIA	Ahmad et al. (2020) ²⁹
β-sitosterol	Inhibited synovial angiogenesis by suppressing endothelial cells proliferation and migration	CIA	Qian et al. (2022) ³⁰
β-sitosterol	Repressed the M1 polarization and augmented M2 polarization	mø	Liu et al. (2019) ³⁵
quercetin	inhibited the release of proinflammatory cytokine (IL-6, TNF-α, IL-1β, IL-8, IL-13, IL-17) by activating SIRT1	CIA	Shen et al. (2021) ³⁶
	Inhibited NLRP3 inflammasome activation and activated HO-1-mediated anti-inflammatory response via modulating the Th17/Treg balance	CIA	Yang et al. (2017) ³⁷
	Inhibited IL-17 and RANKL production, suppressed Th17 cell	FLS	Kim, et al. (2019) ³²
	Modulated the immune response to arthritis via attenuation of the purinergic system (E-NTPDase and E-ADA activities) and the levels of IFN-γ and IL-4	CFA	Saccol et al. (2019) ³⁸
kaempferol	Inhibited RAFLS proliferation, migration, and inflammatory cytokines by suppressing FGFR3-RSK2 signaling	FLS	Lee et al. (2018) ³³
	Inhibited RAFLS migration and invasion by blocking MAPK pathway	FLS	Pan et al. (2017) ³⁹
	Enhanced the suppressive function of Treg cells by inhibiting FOXP3 phosphorylation	Treg	Lin et al. (2015) ⁴⁰
	Inhibited RAFLS proliferation, reduced MMPs, COX-2, and PGE2 production, inhibited NF-κB activation	FLS	Yoon et al. (2013) ⁴¹

optosis, inflammation and immune response and has been confirmed as an important component of the pathogenesis of RA ⁴². TNF is an important therapeutic target for RA and TNF agents were the first molecular targeting drugs developed for the treatment of RA ^43,44. IL6 and IL 10 are critical for the development and progression of RA by affecting the inflammatory process, osteoclast-mediated bone resorption and pannus development ^45,46. TP53 has been found to be involved in the proliferation, apoptosis, cytokine production, and inflammation of RA-FLSs ⁴⁷. Studies with MAPK1, AKT1, and ESR1 have focused on cancer, whereas those on RA have focused on deficiency. However, it has been shown that MAPK1 and AKT1 can be influenced by drugs and upstream signals to affect the proliferation and inflammatory response of RA-FLSs ^48,49. ESR1, as one of two main types of estrogen receptor, may be associated with a high prevalence of RA in females ⁵⁰. The antirheumatic effect of compounds such as quercetin and kaempferol is partially associated with these target genes. For example, quercetin, a natural substance, can induce mitochondrial apoptosis of RA-FLSs through the p53 mechanism ⁵¹. These results therefore illustrate the range of interactions between the multi-components and multi-targets of HDW in the treatment of RA.

KEGG pathway analysis indicated that the 85 therapeutic targets were enriched in viral infection and cancer, such as Kaposi’s sarcoma-associated herpesvirus infection, human cytomegalovirus infection, human papillomavirus infection, and prostate cancer. The evidence that viral infection such as human cytomegalovirus infection ⁵² contributes to RA is strong, and that RA is associated with an increased risk of cancer ⁵³. Additionally, the KEGG analysis results also indicated that the AGE-RAGE, TNF, IL-17, and PI3K-Akt signaling pathways may be critical in the network pharmacology. RAGE is associated with the pathogenesis of diabetes, cardiovascular diseases, neurodegenerative disorders, and cancer ⁵⁴. Upregulation of RAGE can be activated through de novo synthesis of p65 mRNA and the increased availability of transcriptionally active NF-κB perpetuates the cellular inflammatory response ⁵⁵. TNF and IL17 are now widely regarded as playing critical roles in RA pathogenesis, and provide therapeutic targets for the induction and maintenance of inflammation ⁵⁶. It has been shown that the PI3K/AKT signaling pathway, the key molecular mechanism of the occurrence and development of RA, affects synovial inflammation ⁵⁷, synovial angiogenesis ⁵⁸, chondrocyte proliferation, apoptosis and autophagy ⁵⁹, and migration and invasion of fibroblast-like synoviocytes in RA ⁶⁰.

As noted above, RELA, TNF, IL6, TP53, MAPK1, AKT1, IL10, and ESR1 are the top 8 hub genes in the PPI network. Additionally, however, potential targets were found to be involved in multiple pathways in the KEGG enrichment analysis. This indicates that HDW may exert its anti-RA effects by the combined interaction of multi-pathways and multi-targets. Also, the molecular docking analysis was carried out to investigate the interaction of 5 compounds and 8 targets. For example, the absolute value of binding energy about β-sitosterol and ESR1 is the highest in each group, indicating that β-sitosterol has a higher binding affinity than other target genes. Although there have been relatively few studies with stigmasterol and RA, the docking results indicated that stigmasterol performed good binding activity with TP53, ESR1, and IL10. In brief, The higher the binding affinity of components and targets, the more likely HDW treatment of RA will be achieved by modulating several related targets.

Active Components and Potential Targets of HDW. Active components and potential targets of HDW were selected on the basis of their oral bioavailability (OB ≥ 30%) and drug-likeness (DL ≥ 0.18) from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https://old.tcmsp-e.com/tcmsp.php) ⁶¹. An exhaustive search for components was performed using PubMed (https://pubmed.ncbi.nlm.nih.gov/) and CNKI (https://www.cnki.net/). The active components of HDW were determined by SwissADME database (http://www.swissadme.ch/). Afterward, the targets (probability ≥ 0.9) corresponding to the components were screened from the SwissTargetPrediction database (http://swisstargetprediction.ch/) ⁶². Duplicates were deleted to obtain potential targets for further analysis.

Potential Targets of Rheumatoid Arthritis. Potential RA-related therapeutic targets were collected using keywords “rheumatoid arthritis” as a query from the OMIM (https://www.omim.org/) ⁶³, DrugBank (https://go.drugbank.com/) ⁶⁴, TTD (http://db.idrblab.net/ttd/) ⁶⁵, GeneCards (https://www.genecards.org/) ⁶⁶ and DisGeNET (https://www.disgenet.org/) databases ⁶⁷. All acquired targets were imported into UniProt (https://www.uniprot.org/) for normalization and removal of duplicate and erroneous targets.

Common Targets and Construction of the Compound-Target-RA Network. Targets related to the active components of HDW were mapped to RA-related targets to identify ones common to both. These were considered as targets of HDW on treating RA. Following compilation of these, the “compound-target-RA” network was established using Cytoscape 3.9.0 software to determine the interaction relationships ⁶⁸and to further screen for key active components by calculating the extent of these.

Gene Ontology and Pathway Enrichment Analysis. Gene Ontology (GO) analysis and KEGG pathway analyses were performed using the R 3.6.3 and related R packages (clusterProfiler, Org.Hs.eg.db, and ggplot2) ⁶⁹. P.adjusted values ༜ 0.01 were considered to be statistically significant.

Network Visualization and Identification of Hub targets. To further identify interactive relationships among the common target proteins, the protein list was mapped to the STRING database (https://cn.string-db.org/) ⁷⁰. To ensure reliability, we used a cutoff ≥ 0.9 (high-confidence interaction score) to obtain the significant PPIs in network visualization. The CytoNCA plug-in was used to explore hub targets, and the top 8 were generated using Betweenness (BC), Closeness (CC), Degree (DC), and Network (NC) methods.

Validation of the Expression of the Hub Targets. To verify the normalized expression of hub targets against RA in the control and RA groups, we downloaded the gene expression profiles in GSE29746 (synobial fibroblasts of 9 RA patients and 11 healthy controls), GSE56649 (CD4⁺T cells of 13 RA patients and 9 healthy controls), and GSE97779 (macrophages from synovial fluids of 9 RA patients and 5 healthy controls) from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). The platform of the GSE56649 and GSE97779 datasets was Affymetrix GPL570 platform, and that of GSE29746 Affymetrix GPL4133. The RA-related datasets were analyzed with the application of the R language limma package. Furthermore, considering that gene expression was not always consistent with its protein level, we collected the protein expression data of hub targets in different cells based on the Human Protein Atlas (HPA, https://www.proteinatlas.org/).

Molecular Docking. To investigate the associations between the Active Components and hub targets, we applied molecular docking analysis. The mol2 structure files of the 5 main components were downloaded from TCMSP. The crystal structure of the 8 hub targets were obtained from the Protein Data Bank (PDB, https://www.rcsb.org/). Molecular docking studies were carried out with AutoDock 4.0 software. After docking, the results were carried out by sorting the binding energy predicted by docking conformations. The lower the affinity score is, the better the binding effect with a nding energy of less than − 5.0 kcal/mol defined as dependable binding. Binding modes were visualized with the program Pymol.

This study explored the anti-RA mechanism of HDW based on network pharmacology and molecular docking. Five active components and 8 core targets of HDW were finally screened. Four key signaling pathways were enriched by KEGG analysis in addition to virus and cancer-related pathways. Molecular docking showed that the active ingredients successfully docked with related targets. In summary, HDW may affect target genes (RELA, TNF, IL6, TP53, MAPK1, AKT1, IL10, and ESR1) through multiple signaling pathways (AGE-RAGE, TNF, IL-17, and PI3K-Akt), thereby affecting the pathological process of RA (synovial inflammation, synovial angiogenesis, cell proliferation, cell apoptosis, cell autophagy, cell migration, and cell invasion) and ultimately inhibiting the occurrence and development of RA. Our findings provide a theoretical basis for the use of HDW as a therapeutic for RA.

Acknowledgements

The authors wish to thank the School of Chinese Pharmaceutical Sciences and Chinese Medicine Resources of China Medical University for their assistance and cooperation during the present study.

Author contributions statement

Conceptualization, H.-D. and W.-K.S.; Investigation, H.-D.; Methodology, H.-D.; Supervision, W.-K.S.; Visualization, H.-D. and J.J; Writing—Original draft, H.-D.; Writing—Review and editing, W.-K.S., Q.-L.Z., and L.-J.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by grants from the National Natural Science Foundation of China (31701104).

Competing interests

The authors declare no competing interest.

McInnes, I. B., & Schett, G. The pathogenesis of rheumatoid arthritis. N Engl J Med. 365, 2205-2219. DOI: 1056/NEJMra1004965 (2011).
Basile, M., Ciurleo, R., Bramanti, A., Petralia, M., Fagone, P., Nicoletti, F., & Cavalli, E. Cognitive decline in rheumatoid arthritis: insight into the molecular pathogenetic mechanisms. Int J Mol Sci. 22. DOI: 3390/ijms22031185 (2021).
Guo, Y., Wu, Q., Ni, B., Mou, Z., Jiang, Q., Cao, Y., Dong, H., & Wu, Y. Tryptase is a candidate autoantigen in rheumatoid arthritis. 142, 67-77. DOI: 10.1111/imm.12197 (2014).
Jin, S., Li, M., Fang, Y., Li, Q., Liu, J., Duan, X., Liu, Y., Wu, R., Shi, X., Wang, Y., Jiang, Z., Wang, Y., Yu, C., Wang, Q., Tian, X., Zhao, Y., & Zeng, X. Chinese registry of rheumatoid arthritis (CREDIT): II. prevalence and risk factors of major comorbidities in Chinese patients with rheumatoid arthritis. Arthritis Res Ther. 19, 251. DOI: 1186/s13075-017-1457-z (2017).
Matsumoto, T., Takahashi, N., Kojima, T., Yoshioka, Y., Ishikawa, J., Furukawa, K., Ono, K., Sawada, M., Ishiguro, N., & Yamamoto, A. Soluble Siglec-9 suppresses arthritis in a collagen-induced arthritis mouse model and inhibits M1 activation of RAW264.7 macrophages. Arthritis Res Ther. 18, 133. DOI: 1186/s13075-016-1035-9 (2016).
Huber, L. C., Distler, O., Tarner, I., Gay, R. E., Gay, S., & Pap, T. Synovial fibroblasts: key players in rheumatoid arthritis. 45, 669-675. DOI: 10.1093/rheumatology/kel065(2006).
Noss, E. H., & Brenner, M. B. The role and therapeutic implications of fibroblast-like synoviocytes in inflammation and cartilage erosion in rheumatoid arthritis. Immunol Rev. 223, 252-270. DOI: 1111/j.1600-065X.2008.00648.x (2008).
Olsen, N. J., & Stein, C. M. New drugs for rheumatoid arthritis. N Engl J Med. 350, 2167-2179. DOI: 1056/NEJMra032906(2004)
Shetty, A., Hanson, R., Korsten, P., Shawagfeh, M., Arami, S., Volkov, S., Vila, O., Swedler, W., Shunaigat, A. N., Smadi, S., Sawaqed, R., Perkins, D., Shahrara, S., & Sweiss, N. J. Tocilizumab in the treatment of rheumatoid arthritis and beyond. Drug Des Devel Ther. 8, 349-364. DOI: 2147/DDDT.S41437(2014).
Obiri, D. D., Osafo, N., Ayande, P. G., & Antwi, A. O. Xylopia aethiopica (Annonaceae) fruit extract suppresses Freund's adjuvant-induced arthritis in Sprague-Dawley rats. J Ethnopharmacol. 152, 522-531. DOI: 1016/j.jep.2014.01.035 (2014).
Pan, B., Shi, X., Ding, T., & Liu, L. Unraveling the action mechanism of by a network pharmacology approach. Am J Transl Res. 11, 6790-6811. https://pubmed.ncbi.nlm.nih.gov/31814888(2019).
Hu, C.-J., He, J., Li, G.-Z., Fang, P.-P., Xie, J.-D., Ding, Y.-W., Mao, Y.-Q., & Hu, K.-F. Analyzing hedyotis diffusa mechanisms of action from the genomics perspective. Comput Methods Programs Biomed. 174, 1-8. DOI: 1016/j.cmpb.2018.10.019 (2019).
Pu, F., Chen, F., Lin, S., Chen, S., Zhang, Z., Wang, B., & Shao, Z. The synergistic anticancer effect of cisplatin combined with Oldenlandia diffusa in osteosarcoma MG-63 cell line in vitro. Onco Targets Ther. 9, 255-263. DOI: 2147/OTT.S90707 (2016).
Zhang, R., Ma, C., Wei, Y., Wang, X., Jia, J., Li, J., Li, K., Cao, G., & Yang, P. Isolation, purification, structural characteristics, pharmacological activities, and combined action of Hedyotis diffusa polysaccharides: A review. Int J Biol Macromol. 183, 119-131. DOI: 1016/j.ijbiomac.2021.04.139 (2021).
Chen, R., He, J., Tong, X., Tang, L., & Liu, M. The Hedyotis diffusa Willd. (Rubiaceae): A review on phytochemistry, pharmacology, quality control and pharmacokinetics. 21. DOI: 10.3390/molecules21060710 (2016).
Wei, Z., Zhang, J., He, X., Ma, S., Zhang, A., Cui, W., Li, Y., Cai, X., Zhang, S., & Fan, J. Preparation of graphene-multi-walled carbon nanotube composite for quantitive determination of 2-hydroxy-3-Methylanthraquinone in Hedyotis diffusa. J. Electrochem. Sci. 12, 629-638. Google(dailyheadlines.cc) (2017).
Qian, K., Fu, D., Jiang, B., Wang, Y., Tian, F., Song, L., & Li, L. Mechanism of Hedyotis diffusa in the treatment of cervical cancer. Front Pharmacol. 12, 808144. DOI: 3389/fphar.2021.808144 (2021).
Feng, J., Jin, Y., Peng, J., Wei, L., Cai, Q., Yan, Z., Lai, Z., & Lin, J. Hedyotis Diffusa Willd extract suppresses colorectal cancer growth through multiple cellular pathways. Oncol Let. 14, 8197-8205. DOI: 3892/ol.2017.7244 (2017).
Wang, C., Zhou, X., Wang, Y., Wei, D., Deng, C., Xu, X., Xin, P., & Sun, S. The antitumor constituents from Hedyotis diffusa Willd. 22, 2101. DOI: 10.3390/molecules22122101 (2017).
Kim, S.-J., Kim, Y.-G., Kim, D.-S., Jeon, Y.-D., Kim, M.-C., Kim, H.-L., Kim, S.-Y., Jang, H.-J., Lee, B.-C., Hong, S.-H., & Um, J.-Y. Oldenlandia diffusa Ameliorates Dextran Sulphate Sodium-Induced Colitis Through Inhibition of NF-κB Activation. Am J Chin Med. 39, 957-969. DOI: 1142/S0192415X11009330 (2011).
He, J., Lu, X., Wei, T., Dong, Y., Cai, Z., Tang, L., & Liu, M. Asperuloside and asperulosidic acid exert an anti-inflammatory effect via suppression of the NF-κB and MAPK Signaling Pathways in LPS-Induced RAW 264.7macrophages. Int J Mol Sci. 19, 2027. DOI: 3390/ijms19072027 (2018).
He, J., Li, J., Liu, H., Yang, Z., Zhou, F., Wei, T., Dong, Y., Xue, H., Tang, L., & Liu, M. Scandoside exerts anti-inflammatory effect via suppressing NF-κB and MAPK Signaling pathways in LPS-Induced RAW 264.7 macrophages. Int J Mol Sci. 19, 457. DOI: 3390/ijms19020457 (2018).
Kang, S.-Y., Yoon, S.-Y., Roh, D.-H., Jeon, M.-J., Seo, H.-S., Uh, D.-K., Kwon, Y.-B., Kim, H.-W., Han, H.-J., Lee, H.-J., & Lee, J.-H. The anti-arthritic effect of ursolic acid on zymosan-induced acute inflammation and adjuvant-induced chronic arthritis models. J Pharm Pharmacol. 60, 1347-1354. DOI: 1211/jpp/60.10.0011 (2008).
Zhu, H., Liang, Q.-H., Xiong, X.-G., Wang, Y., Zhang, Z.-H., Sun, M.-J., Lu, X., & Wu, D. Anti-inflammatory effects of p-Coumaric Acid, a natural compound of , on arthritis model rats. Evid Based Complement Alternat Med. 2018, 5198594. DOI: 1155/2018/5198594 (2018).
Zhu, H., Liang, Q.-H., Xiong, X.-G., Chen, J., Wu, D., Wang, Y., Yang, B., Zhang, Y., Zhang, Y., & Huang, X. Anti-inflammatory effects of the bioactive compound ferulic acid contained in oldenlandia diffusa on Collagen-Induced Arthritis in rats. Evid Based Complement Alternat Med. 2014, 573801. DOI: 1155/2014/573801(2014).
Jia, P., Liu, W., Liu, S., & Gao, W. Therapeutic effects of Hedyotis diffusa Willd. on type II collagen-induced rheumatoid arthritis in rats. Chinese Journal of Applied Physiology. 34, 558-561. DOI: 12047/j.cjap.5665.2018.125 (2018).
Ning, K., Zhao, X., Poetsch, A., Chen, W.-H., & Yang, J. Computational molecular networks and network pharmacology. Biomed Res Int. 2017, 7573904. DOI: 1155/2017/7573904 (2017).
Niu, B., Zhang, H., Li, C., Yan, F., Song, Y., Hai, G., Jiao, Y., & Feng, Y. Network pharmacology study on the active components of and the mechanism of their effect against cerebral ischemia. Drug Des Devel Ther. 13, 3009-3019. DOI: 2147/DDDT.S207955 (2019).
Ahmad Khan, M., Sarwar, A. H. M. G., Rahat, R., Ahmed, R. S., & Umar, S. Stigmasterol protects rats from collagen induced arthritis by inhibiting proinflammatory cytokines. Int Immunopharmacol. 85, 106642. DOI: 1016/j.intimp.2020.106642 (2020).
Liu, R., Hao, D., Xu, W., Li, J., Li, X., Shen, D., Sheng, K., Zhao, L., Xu, W., Gao, Z., Zhao, X., Liu, Q., & Zhang, Y. β-Sitosterol modulates macrophage polarization and attenuates rheumatoid inflammation in mice. Pharm Biol. 57, 161-168. DOI: 1080/13880209.2019.1577461(2019).
Goyal, A., & Agrawal, N. Quercetin: A potential candidate for the treatment of arthritis. Curr Mol Med. DOI: 2174/1566524021666210315125330 (2021).
Kim, H.-R., Kim, B.-M., Won, J.-Y., Lee, K.-A., Ko, H. M., Kang, Y. S., Lee, S.-H., & Kim, K.-W. Quercetin, a plant polyphenol, has potential for the prevention of bone destruction in rheumatoid arthritis. J Med Food. 22, 152-161. DOI: 1089/jmf.2018.4259(2019).
Lee, C.-J., Moon, S.-J., Jeong, J.-H., Lee, S., Lee, M.-H., Yoo, S.-M., Lee, H. S., Kang, H. C., Lee, J. Y., Lee, W. S., Lee, H.-J., Kim, E.-K., Jhun, J.-Y., Cho, M.-L., Min, J.-K., & Cho, Y.-Y. Kaempferol targeting on the fibroblast growth factor receptor 3-ribosomal S6 kinase 2 signaling axis prevents the development of rheumatoid arthritis. Cell Death Dis. 9, 401. DOI: 1038/s41419-018-0433-0 (2018).
Chen, S., Jiang, S., Zheng, W., Tu, B., Liu, S., Ruan, H., & Fan, C. RelA/p65 inhibition prevents tendon adhesion by modulating inflammation, cell proliferation, and apoptosis. Cell Death Dis 8(3), e2710. doi:10.1038/cddis.2017.135 (2017).
Giridharan, S., & Srinivasan, M. Mechanisms of NF-κB p65 and strategies for therapeutic manipulation. J Inflamm Res. 11, 407-419. DOI: 2147/JIR.S140188 (2018).
Yamanaka, H. TNF as a target of inflammation in rheumatoid arthritis. Endocr Metab Immune Disord Drug Targets 15, 129-134. https://pubmed.ncbi.nlm.nih.gov/25772178(2015).
Feldmann, M. Development of anti-TNF therapy for rheumatoid arthritis. Nat Rev Immunol. 2, 364-371. DOI: 1038/nri802 (2002).
Mollazadeh, H., Cicero, A. F. G., Blesso, C. N., Pirro, M., Majeed, M., & Sahebkar, A. Immune modulation by curcumin: The role of interleukin-10. Crit Rev Food Sci Nutr. 59, 89-101. DOI: 1080/10408398.2017.1358139 (2019).
Pandolfi, F., Franza, L., Carusi, V., Altamura, S., Andriollo, G., & Nucera, E. Interleukin-6 in rheumatoid arthritis. Int J Mol Sci. 21, 5238. DOI: 3390/ijms21155238 (2020).
Taghadosi, M., Adib, M., Jamshidi, A., Mahmoudi, M., & Farhadi, E. The p53 status in rheumatoid arthritis with focus on fibroblast-like synoviocytes. Immunol Res. 69, 225-238. DOI: 1007/s12026-021-09202-7 (2021).
Chen, J., Luo, X., Liu, M., Peng, L., Zhao, Z., He, C., & He, Y. Silencing long non-coding RNA NEAT1 attenuates rheumatoid arthritis via the MAPK/ERK signalling pathway by downregulating microRNA-129 and microRNA-204. RNA Biol. 18, 657-668. DOI: 1080/15476286.2020.1857941 (2021).
Chen, Y., Wang, Y., Liu, M., Zhou, B., & Yang, G. Diosmetin exhibits anti-proliferative and anti-inflammatory effects on TNF-α-stimulated human rheumatoid arthritis fibroblast-like synoviocytes through regulating the Akt and NF-κB signaling pathways. Phytother Res. 34, 1310-1319. DOI: 1002/ptr.6596 (2020).
Dziedziejko, V., Kurzawski, M., Safranow, K., Drozdzik, M., Chlubek, D., & Pawlik, A. Oestrogen receptor polymorphisms in female patients with rheumatoid arthritis. Scand J Rheumatol. 40, 329-333. DOI: 3109/03009742.2011.563752(2011).
Xiao, P., Hao, Y., Zhu, X., & Wu, X. p53 contributes to quercetin-induced apoptosis in human rheumatoid arthritis fibroblast-like synoviocytes. 36, 272-278. DOI: 10.1007/s10753-012-9543-5 (2013).
Davignon, J.-L., Combe, B., & Cantagrel, A. Cytomegalovirus infection: friend or foe in rheumatoid arthritis? Arthritis Res Ther. 23, 16. DOI: 1186/s13075-020-02398-3 (2021).
Bhandari, B., Basyal, B., Sarao, M. S., Nookala, V., & Thein, Y. Prevalence of cancer in rheumatoid arthritis: epidemiological study based on the national health and nutrition examination survey (NHANES). 12, e7870. DOI: 10.7759/cureus.7870 (2020).
Waghela, B. N., Vaidya, F. U., Ranjan, K., Chhipa, A. S., Tiwari, B. S., & Pathak, C. AGE-RAGE synergy influences programmed cell death signaling to promote cancer. Mol Cell Biochem. 476, 585-598. DOI: 1007/s11010-020-03928-y (2021).
Bierhaus, A., & Nawroth, P. P. Multiple levels of regulation determine the role of the receptor for AGE (RAGE) as common soil in inflammation, immune responses and diabetes mellitus and its complications. 52, 2251-2263. DOI: 10.1007/s00125-009-1458-9 (2009).
Noack, M., & Miossec, P. Selected cytokine pathways in rheumatoid arthritis. Semin Immunopathol. 39, 365-383. DOI: 1007/s00281-017-0619-z (2017).
Tu, Y., Tan, L., Lu, T., Wang, K., Wang, H., Han, B., Zhao, Y., Chen, H., Li, Y., Chen, H., Chen, M., & He, C. Glytabastan B, a coumestan isolated from Glycine tabacina, alleviated synovial inflammation, osteoclastogenesis and collagen-induced arthritis through inhibiting MAPK and PI3K/AKT pathways. Biochem Pharmacol. 197, 114912. DOI: 1016/j.bcp.2022.114912 (2022).
Ao, L., Gao, H., Jia, L., Liu, S., Guo, J., Liu, B., & Dong, Q. Matrine inhibits synovial angiogenesis in collagen-induced arthritis rats by regulating HIF-VEGF-Ang and inhibiting the PI3K/Akt signaling pathway. Mol Immunol. 141, 13-20. DOI: 1016/j.molimm.2021.11.002 (2022).
Feng, F.-B., & Qiu, H.-Y. Effects of Artesunate on chondrocyte proliferation, apoptosis and autophagy through the PI3K/AKT/mTOR signaling pathway in rat models with rheumatoid arthritis. Biomed Pharmacother. 102, 1209-1220. DOI: 1016/j.biopha.2018.03.142 (2018).
Ma, J.-D., Jing, J., Wang, J.-W., Yan, T., Li, Q.-H., Mo, Y.-Q., Zheng, D.-H., Gao, J.-L., Nguyen, K.-A., & Dai, L. A novel function of artesunate on inhibiting migration and invasion of fibroblast-like synoviocytes from rheumatoid arthritis patients. Arthritis Res Ther. 21, 153. DOI: 1186/s13075-019-1935-6 (2019).
Qian, K., Zheng, X.-X., Wang, C., Huang, W.-G., Liu, X.-B., Xu, S.-D., Liu, D.-K., Liu, M.-Y., & Lin, C.-S. β-Sitosterol inhibits rheumatoid synovial angiogenesis through suppressing VEGF signaling pathway. Front Pharmacol. 12, 816477. DOI: 3389/fphar.2021.816477 (2021).
Shen, P., Lin, W., Ba, X., Huang, Y., Chen, Z., Han, L., Qin, K., Huang, Y., & Tu, S. Quercetin-mediated SIRT1 activation attenuates collagen-induced mice arthritis. J Ethnopharmacol. 279, 114213. DOI: 1016/j.jep.2021.114213 (2021).
Yang, Y., Zhang, X., Xu, M., Wu, X., Zhao, F., & Zhao, C. Quercetin attenuates collagen-induced arthritis by restoration of Th17/Treg balance and activation of Heme Oxygenase 1-mediated anti-inflammatory effect. Int Immunopharmacol. 54, 153-162. DOI: 1016/j.intimp.2017.11.013 (2018).
Saccol, R. d. S. P., da Silveira, K. L., Adefegha, S. A., Manzoni, A. G., da Silveira, L. L., Coelho, A. P. V., Castilhos, L. G., Abdalla, F. H., Becker, L. V., Martins, N. M. B., Oliveira, J. S., Casali, E. A., & Leal, D. B. R. Effect of quercetin on E-NTPDase/E-ADA activities and cytokine secretion of complete Freund adjuvant-induced arthritic rats. Cell Biochem Funct. 37, 474-485. DOI: 1002/cbf.3413 (2019).
Pan, D., Li, N., Liu, Y., Xu, Q., Liu, Q., You, Y., Wei, Z., Jiang, Y., Liu, M., Guo, T., Cai, X., Liu, X., Wang, Q., Liu, M., Lei, X., Zhang, M., Zhao, X., & Lin, C. Kaempferol inhibits the migration and invasion of rheumatoid arthritis fibroblast-like synoviocytes by blocking activation of the MAPK pathway. Int Immunopharmacol. 55, 174-182. DOI: 1016/j.intimp.2017.12.011 (2018).
Lin, F., Luo, X., Tsun, A., Li, Z., Li, D., & Li, B. Kaempferol enhances the suppressive function of Treg cells by inhibiting FOXP3 phosphorylation. Int Immunopharmacol. 28, 859-865. DOI: 1016/j.intimp.2015.03.044 (2015).
Yoon, H.-Y., Lee, E.-G., Lee, H., Cho, I. J., Choi, Y. J., Sung, M.-S., Yoo, H.-G., & Yoo, W.-H. Kaempferol inhibits IL-1β-induced proliferation of rheumatoid arthritis synovial fibroblasts and the production of COX-2, PGE2 and MMPs. Int J Mol Med. 32, 971-977. DOI: 3892/ijmm.2013.1468 (2013).
Ru, J., Li, P., Wang, J., Zhou, W., Li, B., Huang, C., Li, P., Guo, Z., Tao, W., Yang, Y., Xu, X., Li, Y., Wang, Y., & Yang, L. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 6, 13. DOI: 1186/1758-2946-6-13 (2014).
Daina, A., Michielin, O., & Zoete, V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 47, W357-W364. DOI: 1093/nar/gkz382 (2019).
Hamosh, A., Scott, A. F., Amberger, J., Valle, D., & McKusick, V. A. Online Mendelian Inheritance in Man (OMIM). Hum Mutat. 15, 57-61. https://pubmed.ncbi.nlm.nih.gov/10612823(2000).
Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., Assempour, N., Iynkkaran, I., Liu, Y., Maciejewski, A., Gale, N., Wilson, A., Chin, L., Cummings, R., Le, D., Pon, A., Knox, C., & Wilson, M. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074-D1082. DOI: 1093/nar/gkx1037 (2018).
Li, Y. H., Yu, C. Y., Li, X. X., Zhang, P., Tang, J., Yang, Q., Fu, T., Zhang, X., Cui, X., Tu, G., Zhang, Y., Li, S., Yang, F., Sun, Q., Qin, C., Zeng, X., Chen, Z., Chen, Y. Z., & Zhu, F. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res. 46, D1121-D1127. DOI: 1093/nar/gkx1076 (2018).
Stelzer, G., Rosen, N., Plaschkes, I., Zimmerman, S., Twik, M., Fishilevich, S., Stein, T. I., Nudel, R., Lieder, I., Mazor, Y., Kaplan, S., Dahary, D., Warshawsky, D., Guan-Golan, Y., Kohn, A., Rappaport, N., Safran, M., & Lancet, D. The genecards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics. 54, 1.30.1-1.30.33. DOI: 1002/cpbi.5(2016).
Piñero, J., Ramírez-Anguita, J. M., Saüch-Pitarch, J., Ronzano, F., Centeno, E., Sanz, F., & Furlong, L. I. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 48, D845-D855. DOI: 1093/nar/gkz1021 (2020).
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498-2504. https://pubmed.ncbi.nlm.nih.gov/14597658(2003).
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. 16, 284-287. DOI: 10.1089/omi.2011.0118 (2012).
Szklarczyk, D., Morris, J. H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., Santos, A., Doncheva, N. T., Roth, A., Bork, P., Jensen, L. J., & von Mering, C. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 45, D362-D368. DOI: 1093/nar/gkw937 (2017).

No competing interests reported.

Supplement.pdf

Download PDF

Journal Publication

published 25 Jan, 2023

Read the published version in Scientific Reports →

Editorial decision: Major revision
12 May, 2022
Editor assigned by journal
11 May, 2022
Editor invited by journal
11 May, 2022
Submission checks completed at journal
11 May, 2022
First submitted to journal
07 May, 2022

You are reading this latest preprint version

Network Pharmacology and Bioinformatics Identify the Potential Mechanism Underlying the Use of Hedyotis diffusa Willd for Treatment of Rheumatoid Arthritis

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Results

Discussion

Methods

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1