Skip to main content
Download PDF

Network pharmacology and in vivo experiments reveal the pharmacological effects and molecular mechanisms of Simiao Powder in prevention and treatment for gout

BMC Complementary Medicine and Therapies volume 22, Article number: 152 (2022) Cite this article

Abstract

Background

Gout is a common disease with high incidence due to unhealthy diet and living habits. Simiao Powder, as a classic formula consisted of four common herbs, has been widely used in clinical practice since ancient times to prevent and treat gout. However, the pharmacological mechanism of Simiao Powder is still unclear.

Methods

Based on network pharmacology, Simiao Powder active compounds were identified in TCMSP, ETCM and BATMAN database, used to establish a network of interaction between potential targets of Simiao Powder and known therapeutic targets of gout. Subsequently, the key potential targets are being used for protein–protein interaction, GO enrichment analysis and KEGG pathway enrichment analysis through several authoritative open databases. Molecular docking through AutoDockTools software can verify interaction between molecules. Finally, to validate the predicted results, in vivo experiments based on hyperuricemic-gout mice model were designed and treated with Simiao powder and allopurinol. Serum levels of uric acid (UA), creatinine (Cr), blood urea nitrogen (BUN) and xanthine oxidase (XOD) were determined using a customized assay kit while the expression of PPAR-γ, PTGS1, IL-6 and Bcl2 mRNA were analyzed through qRT-PCR.

Results

Disease-target-compound network was visualized basing on the 20 bioactive compounds and the 19 potential targets using Cytoscape software. The results of PPI analysis, GO enrichment and KEGG pathway enrichment analysis indicate that the potential mechanism of Simiao Powder in treating gout may be achieved by regulating immune and inflammatory reactions, improving metabolism and endocrine. The results of molecular docking show that most of the targets and components have good binding activity. In vivo experiments revealed that Simiao powder can decreased serum UA and XOD levels in hyperuricemic-gout mice, and improved renal function. Furthermore, Simiao powder certainly regulates the expression of PPAR-γ, PTGS1, IL-6 and Bcl2 mRNA in ankle tissue in hyperuricemic-gout mice.

Conclusion

Collectively, this research predicted a multiple compounds, targets, and pathways model mechanism of Simiao Powder in the prevention and treatment of gout, providing new ideas and methods for in-depth research, via vivo experiments.

Peer Review reports

Introduction

Gout is a common chronic disease with urate crystal deposition in and around the joints results from longstanding hyperuricaemia [1, 2]. The general incidence of gout is 1–4% of the general population [3] and the worldwide incidence of gout is reported to increase gradually due to poor dietary habits [4, 5], improved medical care and increased longevity [6]. Some studies [7, 8] with consistent methods of case ascertainment showed a rise more than 1% in the prevalence of gout in US and UK while the prevalence in 2008 was estimated at 1·14% in eastern China, where gout was regarded as a very rare disease in 1980 [5]. Because of the need for long-term treatment, gout not only poses a huge physical and financial burden on patients, but also increases the risk of co-occurring diseases, including cardiovascular [9, 10], metabolic and endocrine diseases [11, 12].

The treatment of gout mainly includes anti-inflammatory and analgesic treatment in acute arthritis, uric acid control in chronic phase and daily personal life management [13]. Medications for acute gout attacks mainly contain colchicine, Non-Steroidal anti-inflammatory Drugs (NSAIDs), steroids and biological agents [13]. Although the above drugs are recommended as first-line drugs for clinical treatment of gout, they are restricted to different degrees due to their respective liver and kidney toxicity and gastrointestinal adverse reactions [3]. Biological agents, such as interleukin-1 blockers, can achieve better therapeutic results with minimal side effects [3, 14], but are difficult to popularize because of their high cost. Treatment in the asymptomatic and hyperuricemia stages of gout aims to control uric acid by reducing uric acid synthesis (including allopurinol and febuxostat) and increasing renal urate excretion (including probenecid, benzbromarone and URAT1 inhibitor) [3]. Lowering uric acid requires long-term persistence, but most of the above drugs are difficult to use for a long time due to toxic and side effects. Therefore, more natural products need to be found to treat gout.

At present, it has become the focus of pharmaceutical research to search for drugs to treat gout and reduce hyperuricemia from Chinese herbs and natural materials [15,16,17]. Recent years, Simiao Powder, which mainly consist of Atractylodes Lancea (Thunb.)Dc, Phellodendri Chinrnsis Cortex, Cyathulae Radix, Coicis Semen, is derived from traditional Chinese medicine and widely used in the clinical treatment of gout in China due to good efficacy and few side effects [18, 19]. Modern pharmacology studies have also shown that Simiao Powder have obvious anti-inflammatory effects and inhibit the activity of xanthine oxidase for gout treatment [20,21,22]. However, the specific mechanism of Simiao Powder in treating gout has not been fully studied. In particular, the treatment of gout by Simiao Powder may be a multi-component-target-pathway model since the pathogenesis of gout is related to immune regulation, inflammatory response and metabolism.

Network pharmacology is a new method of drug research proposed based on multi-directional systematic pharmacology and biology [23,24,25], which combines drug component target network with biological target network from multiple perspectives to study the mechanism of drug action more systematically and comprehensively. Therefore, this study used network pharmacology method to explore the potential mechanism of Simiao Powder in the treatment of gout at multiple levels of compound-target-pathway. The experimental procedures of our study were shown in Fig. 1.

Fig. 1
figure 1

Workflow of the study design

Full size image

Materials and methods

Identification and screening of chemical compounds

To identify the chemical compounds of the four herbs in Simiao Powder, we performed a search in Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform [26] (TCMSP, http://lsp.nwu.edu.cn/tcmsp.php), ETCM [27] (http://www.tcmip.cn/ETCM/index.php/Home), BATMAN [28] (http://bionet.ncps-b.org.cn/batman-tcm) using “Atractylodes Lancea (Thunb.)Dc”, “Phellodendri Chinrnsis Cortex”, “Cyathulae Radix” and “Coicis Semen”. TCMSP, a unique systems pharmacology database for users to comprehensively study Chinese herbal medicines, contains information on all 500 TCM formulae registered in the Chinese Pharmacopoeia (2010 edition), with a total of 30,069 compounds extracted by literature mining and database integration [29].

According to ADME (absorption, distribution, metabolism and excretion) evaluation, two ADME-related models, including the evaluation of oral bioavailability (OB) and drug-likeness (DL), were employed to identify the potential bioactive compounds of Simiao Powder. Oral bioavailability (OB) and drug-likeness (DL) are very important pharmacokinetic parameters and key indicators in ADME process [29]. According to existing studies, OB ≥ 30% and DL ≥ 0.18 were used as screening criteria for drug compounds in this study.

Identification of targets for chemical compounds

The protein targets of the compounds were searched from the TCMSP and Drugbank database [30] (https://www.drugbank.ca/) while the gene names were extracted from UniProt Knowledgebase [31] (UniProtKB, http://www.uniprot.org). The DrugBank database is a bioinformatics and cheminformatics resource that combines detailed drug data with comprehensive drug target information. The UniProtKB is a central hub for the collection of functional information on proteins, with accurate, consistent and rich annotation, includes widely accepted biological ontologies, classifications and cross-references, and clear indications of the quality of annotation in the form of evidence attribution of experimental and computational data.

Identification of known therapeutic targets for gout

Therapeutic targets of gout were collected from four sources, include GeneCards database (https://www.genecards.org), the therapeutic target database [32] (TTD, https://db.idrblab.org/ttd/), Pharmacogenomics Knowledge Base [33] (PharmGKB, https://www.pharmgkb.org/), and the Online Mendelian Inheritance in Man (OMIM) database [34] (http:// www.omim.org/). The three databases mentioned above are commonly used databases for analysis of genomic known diseases with a genetic component and linked them to the relevant genes in the human genome, and they complement each other with their own characteristics. “Gout” was used to search in all the database as the keyword.

Network construction of targets between Simiao Powder and gout

Firstly, the targets of active components of Simiao Powder were summarized, and the data were imported into the Cytoscape 3.6.0 software to construct the network of active component targets. Secondly, gout related gene targets obtained from the database were imported into Cytoscape 3.6.0 software to construct disease target network. Finally, the mapping function in Cytoscape 3.6.0 was used to combine the two networks to obtain the prediction targets that Simiao Powder might play a role in gout. This network can illustrate the relationships between putative targets of Simiao Powder and known therapeutic targets of gout.

Protein–protein interaction

Based on the prediction targets between compounds and disease, protein–protein interaction (PPI) data were generated through two major existing public PPI databases with the species limited as “Homo sapiens”, including Metascape [35] (http://metascape.org/) and String [36] (https://string-db.org). The two open databases covered the majority of known protein–protein interactions information for human.

GO enrichment analysis and pathway enrichment analysis

The gene ontology (GO) enrichment analysis was conducted to further validate whether the potential targets are indeed a match for gout in three levels, contained biological process analysis, molecular function analysis, cell component analysis. The GO enrichment analysis was performed using the functional annotation tool of DAVID Bioinformatics Resources 6.7 [37] (https://david-d.ncifcrf.gov/) and OmicShare platform (https://www.omicshare.com/).

To comprehensively understand the pathway mechanisms of Simiao Powder, the KEGG pathway enrichment analysis was also conducted using the functional annotation tool of DAVID Bioinformatics Resources 6.7 and OmicShare platform. Finally, Target-pathway network for Simiao Powder acting on gout was constructed by Cytoscape 3.6.0 ClueGO.

The pink and blue nodes represent the pathway and targets, respectively, and the edges represent the interactions among them.

Molecular docking verification

The analysis obtained the targets with degree value ≥ 6 in the Disease-Target- Compound Network diagram and the key protein targets from PPI network. These targets were searched for literature, and finally screened out with gout-related targets, docking with the active compounds of Simiao Powder. The binding strength and activity between the target and the active compound were evaluated based on the binding energy and the hydrogen bond.

Animals and preparation of Simiao Powder liquor

Male Kun-Ming mice (20 ± 2 g) was purchased from Liaoning Changsheng Biotechnology Co., Ltd. (Benxi, Liaoning Province, PR China). Animals were housed under a normal 12-h/12-h light/dark schedule, room temperature (22 ± 2 °C), relative humidity (55 ± 5%), and given a standard chow and water ad libitum for the duration of the study. All studies were carried out in accordance with the Institutional Animal Care Committee at the Jinan University. Simiao Powder, consisting of four classical herbs: Atractylodes Lancea (Thunb.)Dc, Phellodendri Chinrnsis Cortex, Cyathulae Radix, Coicis Semen,were purchased from Huarun Sanjiu Medicine Co., Ltd. According to the literature [38], it was recommended that the dosages of the four herbs granules in Simiao Powder were as follows: 15 g, 15 g, 15 g and 30 g. According to the different compatibility ratios of Simiao Powder, the four herbs were mixed, weighing 75 g in total.

Hyperuricemic-gout mice and drug administration

Experimental animal model of Hyperuricemic-Gout Mice induced by uricase inhibitor potassium oxonate has been used to study drug action for many years. Mice were divided into 4 groups: Normal + water(group A), Model + water(group B), Model + Simiao Powder(group C), Model + allopurinol(group D). The normal group was intraperitoneally injected with 0.5% CMC-Na solution, while the other groups were intraperitoneally injected with suspension composed of 300 mg/kg potassium oxazinate and 200 mg/kg hypoxanthine at 15:00 p.m. every day for seven consecutive days (reagents purchased from Sigma, St. Louis, MO, USA). Simiao Powder and allopurinol were orally initiated at 16:00 p.m. on the day by intragastric administration. Dosages of Simiao powder and allopurinol were determined based on the conversions from clinical adult dosages and previous studies. The dosage was as follows [38]: allopurinol 20 mg/kg/day, Simiao Powder 11.3 g/kg/day. The clinical efficacy of Simiao powder was generally evaluated after a week of treatment, so we chose 7 days as the treatment time.

Blood and ankle tissue sample collection

Whole blood samples were collected 1 h after final administration on the seventh day, and then allowed to clot for approximately 1 h at room temperature and centrifuged at 3500 rpm for 10 min to obtain serum, stored at − 80 °C until assayed. Simultaneously, ankle tissue was rapidly and carefully separated on ice-plate and stored at − 80 °C for assays, respectively.

Determination of serum uric acid, creatinine, blood urea nitrogen and xanthine oxidase levels

Serum levels of uric acid (UA), creatinine (Cr), blood urea nitrogen (BUN) and xanthine oxidase (XOD) were determined using a customized assay kit. All assay kits were purchased from Jiancheng Biotech. (Nanjing, PR China). Uric acid concentration in serum was determined by the phosphotungstic acid method. Blood urea nitrogen was determined by rapid urease method while Creatinine was measured using sarcosine oxidase method. Xanthine Oxidase was detected by colorimetric method. The operation method of the experiment shall strictly refer to the manufacturer's instructions.

RT-PCR of PPAR-γ, PTGS1, IL-6, and Bcl2

Related methods are described in our previous article [39]. Briefly, Total RNA was extracted by RNAiso Plus (catalog No.9108; TaKaRa, Kusatsu, Japan) according to the manufacturer’s instructions. The cDNA synthesis and RT-qPCRs were conducted using a CFX Connect Real-Time PCR Detection system (Bio-Rad, Berkeley, CA, USA) with PrimeScript RT reagent kits (RR047A; TaKaRa) and SYBR Premix EX Taq II (RR820A; TaKaRa) according to the manufacturer’s instructions. The primers were synthesized by Generay Biotech (Shanghai, China), and the primers for RT-qPCR are listed in Table 1. The transcript levels were normalized to an internal control (GAPDH).

Table 1 The primers for RT-qPCR
Full size table

Statistical analysis

All data are statistically analyzed using one-way ANOVA analysis with the statistical software SPSS 13.0. Data were expressed as Mean ± Standard Error of the mean(S.E.M.). A value of P < 0.05 was considered statistically significant. Figures were obtained by GraphPad Prism 9 (GraphPad Software, Inc., San Diego, CA).

Results

Active chemical compounds of Simiao Powder

Based on the criteria of OB ≥ 30% and DL ≥ 0.18, a total of 59 compounds of Simiao Powder were retrieved from the TCMSP, including 9 in Atractylodes Lancea (Thunb.)Dc, 4 in Phellodendri Chinrnsis Cortex, 37 in Cyathulae Radix, 9 in Coicis Semen. Among the 59 satisfied compounds, a total of 56 compounds were finally obtained after excluding the duplicates.

Putative targets for Simiao Powder

According to the target prediction system in TCMSP and Drugbank databases, the 56 potential active components of Simiao Powder collected were input into the databases and 824 putative targets were obtained by pairing. The corresponding target proteins were searched through the UniprotKB database for their corresponding gene symbol names.

Known therapeutic targets for gout and disease-drug common targets

A total of 756 targets associated with gout were collected from the four open databases, namely, 734 in GeneCards database, 15 in TTD, 4 in PharmGKB and 3 in OMIM database. After removing the overlapping targets, 746 associated targets were obtained.

The prediction targets of compounds and disease-related targets were combined and intersected to obtain the possible targets of drug treatment for gout. Subsequently, the result showed that gout share 19 targets with 20 bioactive compounds of Simiao Powder (Fig. 2). The 19 targets are the common targets between gout and 20 bioactive compounds. The detailed information of the associated targets and 20 bioactive compounds are provided in Tables 2 and 3, respectively.

Fig. 2
figure 2

Intersection targets of Simiao Powder and gout

Full size image
Table 2 Information regarding gout-related targets of Simiao Powder
Full size table
Table 3 Bioactive Compounds of Simiao Powder related to Gout
Full size table

Disease-target- compound network and analysis

Disease-Target- Compound network was visualized on the basis of the 20 bioactive compounds and the 19 combined targets using Cytoscape software. As shown in Fig. 3, the network is consisted of 41 nodes (1 disease, 1 formula, 19 targets and 20 bioactive compounds). The orange, purple, green and cyan nodes represent the disease, targets, compounds and formula, respectively; the edges represent the interactions among them and nodes sizes are proportional to their degree. Furthermore, network analysis was performed to evaluate the centralization and heterogeneity [29]. The resulting centralization and heterogeneity of the network are 0.412 and 1.170, respectively, indicating that some nodes are more concentrated in the network than others. In brief, the compound-target space is biased toward certain compounds and targets [29], which means that some compounds have multiple targets or the target has multiple compounds, especially the high-degree compounds such as MOL000098 (quercetin, degree = 41), MOL000359 (beta-sitosterol, degree = 9), MOL000449 (stigmasterol, degree = 7), MOL000173 (wogonin, degree = 6) and the high-degree targets such as PTGS1(degree = 22), PPARG(degree = 8), BCL2(degree = 6).

Fig. 3
figure 3

Disease-Target-Compound Network of Simiao Powder and Gout. The orange, purple, green and cyan nodes represent the disease, targets, compounds and formula, respectively; the edges represent the interactions among them and nodes sizes are proportional to their degree

Full size image

Protein–protein interaction analysis

Based on the prediction targets between compounds and disease, protein–protein interaction (PPI) data were generated for the 19 common targets mentioned above (The confidence score > 0.4). The PPI network showed current interactions among the 19 common targets (Fig. 4). Network nodes represent proteins, splice isoforms or post-translational modifications are collapsed, ie. each node represents all the proteins produced by a single, protein-coding gene locus [36]. Edges represent protein–protein associations which are meant to be specific and meaningful and line thickness indicates the strength of data support. As is shown in Fig. 4, the key protein targets include IL-6, PTGS1, PPARG, NFKBIA, AHR, PRKCA and VEGFA.

Fig. 4
figure 4

The PPI network of gout-related targets of Simiao Powder. Note: the circular nodes represent the interacting proteins that directly or indirectly interact with each other. The node size represents the importance of proteins, and the thickness of the line represents the closeness of the relationship between proteins

Full size image

GO enrichment analysis for targets

The gene ontology (GO) enrichment analysis was performed and the results were revealed in three levels via biological process, molecular function and cell component analysis. Figure 5 shows the annotation clustering result of GO enrichment analysis. At the level of molecular function, Simiao Powder has a great influence on protein binding, catalytic activity and transcriptional regulatory activity; at the level of cell components, Simiao Powder has a great influence on cells, mainly including cell parts, cytoplasm, organelles and cell membranes; In terms of biological processes, Simiao Powder mainly affects cellular process, metabolic process, response to stimulus, biological regulation process, immune system process, and multicellular organismal process etc. Figure 6 list the 20 most significantly enriched GO terms of biological process and molecular function respectively. Notably, numerous targets are involved in cellular process, metabolic process, response to stimulus, biological regulation process and immune system process, which are closely related to the pathogenesis of gout.

Fig. 5
figure 5

The annotation clustering result of GO enrichment analysis

Full size image
Fig. 6
figure 6

Top 20 of GO enrichment terms of biological process and molecular function

Full size image

The KEGG pathway enrichment analysis

The KEGG pathway enrichment analysis was performed using the functional annotation tool of DAVID Bioinformatics Resources 6.7 and OmicShare platform. Figure 7 list the 20 most significantly enriched KEGG pathways with RichFactor and Gene Percent respectively. It shows that Simiao Powder achieves its therapeutic effect for gout treatment through multiple pathways, mainly including AGE-RAGE signaling pathway in diabetic complications, Fluid shear stress and atherosclerosis, HIF-1 signaling pathway, NF-kappa B signaling pathway, TNF signaling pathway, EGFR tyrosine kinase inhibitor resistance, Cytosolic DNA-sensing pathway, Th17 cell differentiation Legionellosis, NOD-like receptor signaling pathway、PI3K-Akt signaling pathway. To further elucidate the mechanisms of Simiao Powder for gout treatment, we constructed a functional annotation clustering for significant KEGG pathways as shown in Fig. 8. Moreover, the results reveal that related pathways are mainly concentrated in the aspects of immune and endocrine systems, lipid metabolism, cofactor and vitamin metabolism, signal transduction and cardiovascular diseases, which are considered closely associated with gout.

Fig. 7
figure 7

Top 20 of KEGG pathways enrichment with Rich-Factor and Gene Percent

Full size image
Fig. 8
figure 8

KEGG pathway annotation of gout-related targets of Simiao Powder

Full size image

Finally, Compounds-Targets-pathways network for Simiao Powder acting on gout was constructed shown as Fig. 9.

Fig. 9
figure 9

Compounds-Targets-pathways network for Simiao Powder acting on gout

Full size image

Molecular docking verification

The molecular docking software AutoDockTools can apply semi-flexible docking methods on the basis of complex networks to study the interaction between molecules, and then evaluate the protein–ligand binding potential based on its comprehensive characteristics. There were 3 targets with Degree value ≥ 6 in the disease-target-compound network diagram, and top 3 targets in the PPI network diagram. After removing duplicate targets, we obtained 4 targets in total: IL-6, PTGS1, PPARG, BCL2. These targets were searched in the literature respectively and all targets were related to gout. They were respectively molecularly docked with the potential active compounds of Simiao Powder.

According to the results of comprehensive molecular docking, the minimum binding energy was -3.31 kcal/mol and the maximum is -7.72 kcal/mol. Among them, the 4 potential active compounds of Simiao Powder (quercetin, beta-sitosterol, stigmasterol, wogonin) and the amino acid residues of the targets IL-6, PTGS1, PPARG, BCL2 were combined by intermolecular hydrogen bonds. Most of the targets and components had good binding activity because of high binding energy. This indicates that the main characteristic chemical components of Simiao Powder have good binding ability with the targets IL-6, PTGS1, PPARG and BCL2. In the results of molecular docking, there were 31.25% of them binding energy higher than -5 kcal/mol while others were lower than -5 kcal/mol. In addition, there were two of them lower than -7 kcal/mol. Among them, the binding energy of PPARG and stigmasterol is the highest, -7.72 kcal/mol. The results of molecular docking were shown in Table 4. As shown in Fig. 10, the docking results with binding energy lower than -7 kcal/mol were below, which hydrogen bonds and the binding site were drawn by PyMOL.

Table 4 The results of molecular docking
Full size table
Fig. 10
figure 10

Docking results for molecules with binding energy lower than -7 kcal/mol. Note: (a) the molecular docking result of PPARG and stigmasterol, the number of hydrogen bonds: 1, the binding energy: -7.72 kcal/mol; (b) the molecular docking result of PTGS1 and beta-sitosterol, the number of hydrogen bonds: 1, the binding energy: -7.01 kcal/mol

Full size image

Simiao powder decreased serum UA and XOD levels in hyperuricemic-gout mice

As expected, intraperitoneally injected potassium oxazinate and hypoxanthine significantly elevated serum UA and XOD levels compared with normal mice (Fig. 11a). Simiao powder significantly decreased serum UA levels of hyperuricemic-gout mice to the normal while allopurinol, as a positive control, reduced the levels of hyperuricemic mice, even lower than that of normal group (Fig. 11a). Xanthine oxidase can catalyze hypoxanthine to xanthine and increase uric acid, and also directly catalyze xanthine to uric acid, which has great influence on serum uric acid production. Both allopurinol and Simiao powder can reduce the level of XOD in serum, but only allopurinol has a significant difference, even lower than the normal group (Fig. 11b).

Fig. 11
figure 11

The effects of Simiao powder and allopurinol on serum levels of UA, XOD, BUN and Cr. (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.)

Full size image

Simiao powder improved renal function in hyperuricemic-gout mice

Serum BUN and Cr levels in Hyperuricemic-gout mice were significantly higher than those in the normal group (Fig. 11c-d). Simiao powder can effectively reverse the changes of the above indexes and reduce serum Cr and BUN in hyperuricemia mice to the level close to the normal group (Fig. 11c-d). conversely, allopurinol cannot decrease serum Cr and BUN levels in disease models, even higher than that of model group (Fig. 11c-d).

Simiao powder regulates the expression of PPAR-γ, PTGS1, IL-6 and Bcl2 mRNA in Ankle tissue in hyperuricemic-gout mice

The effects of Simiao powder and allopurinol on mRNA levels of ankle PPAR-γ, PTGS1, IL-6, and Bcl2 in hyperuricemic-gout and normal mice were shown in Fig. 12a-d. Compared with normal mice, the mRNA expression of PPAR-γ was significantly decreased in hyperuricemic-gout mice, but significantly increased after treatment with Chinese medicine or Western medicine, even higher than the normal group. PTGS1 mRNA were increased in hyperuricemic-gout mice compared with normal mice, but there was no significant difference. Both Simiao powder and allopurinol did not reduce the mRNA expression of PTGS1, even higher than model group. Compared with normal mice, the mRNA expression levels of IL-6 and Bcl2 were significantly increased in hyperuricemic-gout mice. Both Simiao powder and allopurinol can down-regulate the expression of IL-6 mRNA, but still not restore to the normal value. In addition, allopurinol can reduce the mRNA expression level of Bcl2 in oxonate-induced hyperuricemic mice, but Simiao powder bring an opposite effect.

Fig. 12
figure 12

The effects of Simiao powder and allopurinol on ankle mRNA levels of PPAR-γ, PTGS1, IL-6 and Bcl2. (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.)

Full size image

Discussion

Traditional Chinese medicine considers gout to be arthralgia syndrome result from the dysfunction of viscera [40], which is caused by the changes of poor dietary habits. There are similarities between Chinese and western medicine in understanding the causes of gout. Moreover, Gout is generally regarded as dampness and heat stagnation in meridians and muscles-bones [40], while Simiao Powder has the effect of clearing heat and dampness, relaxing muscles and clearing collaterals [19], and is a classic prescription for treating dampness and heat.

This research demonstrates that 20 active compounds and 19 potential genetic targets of Simiao Powder in the treatment of gout were found through network pharmacology. Then, specific functions and mechanisms are revealed through protein interaction network analysis, GO enrichment analysis and KEGG pathways enrichment analysis. The results showed that Simiao Powder, through multi-components-targets-pathways mode, play an important role on the immune regulation, inflammatory response, metabolism and endocrine regulation, cell signal transduction, cell growth and apoptosis, so as to achieve anti-inflammatory and analgesia, uric acid lowering and metabolism improvement. Further classification and analysis showed that the therapeutic effects of Simiao Powder on gout mainly focused on several modules, such as immune regulation and inflammatory response, metabolism and endocrine regulation, and cell function. To visually reflect the important pathway diagram and its key targets, they were compressed and drawn in Fig. 13, based on the signal pathway maps of the Kyoto Encyclopedia of Genes and Genomes database [41].

Fig. 13
figure 13

Potential pathways and therapeutic modules between Gout and Simiao Powder. Seven pathways (marked in different colors) constitute the compressed pathway

Full size image

Immune regulation and inflammatory response module

Generally speaking, uric acid crystal deposition in articular cavity is the trigger of gout [3, 13]. These crystals initiate the inflammatory process by being engulfed by synovial phagocytes, leading to the release of lysosomal enzymes and the production of inflammatory chemokines [3]. Another mechanism is that uric acid crystal changes the stability of phagocytes membrane by directly crosslinking with membrane lipids and glycoproteins. The presence of cytokines, to as IL-1β, IL-6 and TNF-a, suggest that these cytokines have a role in granuloma formation in to patients [42]. Colchicine is thought to prevent acute attacks by altering the affinity of selectin on endothelial cells and neutrophils to inflammatory mediators and by blocking eutrophic stimulation induced by endothelial cells [43].

Among the major putative targets and pathways of Simiao Powder, most of them are related to the immune regulation and inflammatory response for gout. At the gene target level, PTGS1 is the key target with the largest number of junctions, usually responds to hormone stimulation rapidly to produce prostaglandins and continuously regulate physiological processes, such as mediating responses to inflammatory stress [44]. Furthermore, deletion of PTGS1 substantially repressed the expressions of NF-κB genes such as IL6, IL8, and SELE [45]. RELA can encodes a nuclear transcription factor-κB (NF-κB) p65 subunit protein and participate in the formation of NF-κB dimers [46]. NF-κB capture in the cytoplasm REL dimers, thus inhibiting dimers NF-kappa B/REL the activity of the complexes. Almost all types cell is affected by transcription factors, which is considered as the end of a series of signal transduction events. Many biological processes such as inflammation, immunity, cell growth and apoptosis are closely related to NF-κB. Another target IL-6 is an important mediator of febrile and acute responses [47]. At the pathway level, five key signaling pathways, namely, NF-kappa B signaling pathway, TNF signaling pathway, Th17 cell differentiation legionel disease, Nod-like receptor signaling pathway and Cytosolic DNA-sensing pathway, were all closely associated with immune and inflammatory reactions.

Metabolism and endocrine regulation module

Gout is defined as a metabolic disorder [2]. Studies suggest that metabolic and endocrine regulation may play a role in gout attacks, and that obesity led to an increase in uric acid and exacerbate the risk of hyperuricemia [48,49,50]. In this study, PTGS1 and PPARG were the major targets related to metabolism and endocrine. PTGS1 is involved in the prostaglandin biosynthesis pathway as part of lipid metabolism [51, 52]. PPARG is a key regulator of fat cell differentiation and glucose homeostasis by regulating the peroxidosome beta oxidation pathway of fatty acids [53, 54]. It can also regulate the homeostasis of the intestinal environment through inflammation mediated by NF-κB [55]. Pathway enrichment results show that metabolic and endocrine pathways are important in the treatment. Especially, AGE-RAGE signaling pathway in diabetic complications is closely related to blood glucose and blood lipids [56]. Moreover, this pathway can further activate NF-κB activity by activating various intracellular signaling pathways of NADPH oxidase, protein kinase C and MAPK [57]. In addition, Vitamin C can increase the renal excretion of uric acid as a supplement during management of gout [58]. The enrichment of cofactor and vitamin metabolism also confirmed this idea.

Cell function module

One mechanism of acute gout inflammation is the noninflammatory phagocytosis of macrophages to dying neutrophils [3, 59]. The pathogenesis involves initial activation of monocytes and mast cells, followed by neutrophils [3, 60, 61]. Well-differentiated macrophages have the ability to swallow urate crystals without provoking an inflammatory response. However, monocytes with low differentiation degree produce a large amount of TNF, IL-1, IL-6 and IL-8 after phagocytosis of urate crystals, and activate endothelial cells [3]. Activation of endothelial cells further exacerbates the inflammatory response and migration of neutrophils. Notably, the key targets of this research mainly related to cell growth, apoptosis and signal transduction. For example, PTGS1 plays an important role in cell protection [62, 63] while AHR participate in the regulation of cell cycle [64]. PRKCA is involved in cell proliferation, apoptosis, differentiation, migration and adhesion [65] while VEGFA can induce endothelial cell proliferation, promote cell migration, inhibit apoptosis and induce vascular through [66]. In terms of signaling pathways, HIF-1 signaling pathway and Cytosolic DNA-sensing pathway play an important role in cell signal transduction, while EGFR tyrosine kinase inhibitor resistance can regulate cell environment homeostasis [67].

Multi-components-targets-pathways treatment model, molecular docking verification and research limitations

These modules have their own unique functions, but they are connected and affect with each other, forming a dynamic adjustment network. That reflects the characteristics of multiple compounds, multiple targets and multiple pathways of Simiao Powder in the treatment of gout. Furthermore, gout is often accompanied by other comorbidities [10, 11, 68, 69] and its treatment needs to be systematic and comprehensive. Many targets and pathways predicted in this study are related to cardiovascular system and metabolic system, which means Simiao Powder can give full consideration to the treatment of complications. In addition, the western medicine treatment of gout usually corresponds to the medication in stages and carefully controls the dosage [3], while Simiao Powder can be used for the treatment of gout in various stages due to its complex functional network.

Molecular docking reveals the interaction between components and targets in the network, thereby improving the accuracy of the network [70]. The negative score of the chemical bond binding energy is used to explain the docking result. The larger the absolute value of the negative score, the more stable the structure. According to the results of molecular docking, the binding energy of PPARG and stigmasterol is the lowest, -7.72 kcal/mol. According to above analysis, PPARG can regulate the homeostasis of the intestinal environment through inflammation, and stigmasterol can exert anti-inflammatory [71] and anti-cancer [72] effects. The binding energy of PTGS1 and beta-sitosterol is -7.01 kcal/mol. PTGS1 can also mediate responses to inflammatory stress. Researches have shown that beta-sitosterol has anti-inflammatory [73], antioxidant [74], anti-cancer [75] functions. According to the results of molecular docking, the ingredients of Simiao Powder can treat gout by exerting anti-inflammatory effects. In addition, the main characteristic chemical components of Simiao Powder have good binding ability with IL-6, PTGS1, PPARG and BCL2 targets, which indicates that the active components of Simiao Powder can effectively treat gout by combining with core targets.

In vivo experiments demonstrated the efficacy of Simiao powder in the treatment of hyperuricemic-gout mice and identified key targets

Simiao powder decreased serum UA and XOD levels in hyperuricemic-gout mice, and improved renal function via regulating serum BUN and Cr levels. It was noted that both Simiao powder and allopurinol can effectively reduce serum uric acid and XOD, but Simiao powder may bring few side effects and improve renal function in the treatment. Our experimental results are consistent with a previous study [38]. In addition, our experiments showed that Simiao powder certainly regulates the expression of PPAR-γ, PTGS1, IL-6 and Bcl2 mRNA in ankle tissue in hyperuricemic-gout mice. These gene targets mentioned above are likely to affect the occurrence and development of gout and hyperuricemia through immune inflammation, apoptosis and endocrine system. However, an in-depth analysis of these targets mentioned above is still needed to elucidate their potential molecular mechanisms in Simiao powder treating gout.

Our research still has some limitations. For example, the actual absorption of drugs may be different from the reality, and drug compounds may react with each other to form new unknown compounds. Furthermore, our screening criteria for drug compounds are based on oral absorption and pharmacodynamics, but some compounds may not be absorbed into the blood but act on intestinal microecology, which is conducive to the regulation of immunity and metabolism.

Conclusion

Collectively, this research predicted a multiple compounds, targets, and pathways model mechanism of Simiao Powder in the prevention and treatment of gout, providing new ideas and methods for in-depth research, via vivo experiments.

Availability of data and materials

The data used to support the findings of this study are available from the corresponding author upon request.

Abbreviations

TCMSP:

Traditional Chinese Medicine Systems Pharmacology

BATMAN-TCM:

Tool for Molecular mechanism of Traditional Chinese Medicine

DAVID:

Database for annotation, visualization and integrated discovery

OMIM:

Online Mendelian Inheritance in Man

PPI:

Protein–protein interaction

OB:

Oral bioavailability

DL:

Drug-likeness

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genome

IL-6:

Interleukin-6

PTGS1:

Prostaglandin-endoperoxide synthase 1

PPARG:

Peroxisome proliferator-activated receptor gamma

Bcl2:

B-cell lymphoma-2

References

  1. Richette P, Bardin T. Gout. Lancet (London, England). 2010;375(9711):318–28.

    Article  CAS  Google Scholar 

  2. Robinson PC. Gout - An update of aetiology, genetics, co-morbidities and management. Maturitas. 2018;118:67–73.

    Article  CAS  PubMed  Google Scholar 

  3. Ragab G, Elshahaly M, Bardin T. Gout: An old disease in new perspective - A review. J Adv Res. 2017;8(5):495–511.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Kuo CF, Grainge MJ, Zhang W, Doherty M. Global epidemiology of gout: prevalence, incidence and risk factors. Nat Rev Rheumatol. 2015;11(11):649–62.

    Article  PubMed  Google Scholar 

  5. Miao Z, Li C, Chen Y, Zhao S, Wang Y, Wang Z, Chen X, Xu F, Wang F, Sun R, et al. Dietary and lifestyle changes associated with high prevalence of hyperuricemia and gout in the Shandong coastal cities of Eastern China. J Rheumatol. 2008;35(9):1859–64.

    PubMed  Google Scholar 

  6. Saag KG, Choi H. Epidemiology, risk factors, and lifestyle modifications for gout. Arthritis Res Ther. 2006;8(Suppl 1):S2.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kuo CF, Grainge MJ, Mallen C, Zhang W, Doherty M. Rising burden of gout in the UK but continuing suboptimal management: a nationwide population study. Ann Rheum Dis. 2015;74(4):661–7.

    Article  PubMed  Google Scholar 

  8. Zhu Y, Pandya BJ, Choi HK. Prevalence of gout and hyperuricemia in the US general population: the National Health and Nutrition Examination Survey 2007–2008. Arthritis Rheum. 2011;63(10):3136–41.

    Article  PubMed  Google Scholar 

  9. Kleber ME, Delgado G, Grammer TB, Silbernagel G, Huang J, Kramer BK, Ritz E, Marz W. Uric Acid and Cardiovascular Events: A Mendelian Randomization Study. J Am Soc Nephrol. 2015;26(11):2831–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Krishnan E, Svendsen K, Neaton JD, Grandits G, Kuller LH. Long-term cardiovascular mortality among middle-aged men with gout. Arch Intern Med. 2008;168(10):1104–10.

    Article  PubMed  Google Scholar 

  11. Katsiki N, Papanas N, Fonseca VA, Maltezos E, Mikhailidis DP. Uric acid and diabetes: Is there a link? Curr Pharm Des. 2013;19(27):4930–7.

    Article  CAS  PubMed  Google Scholar 

  12. Testa A, Mallamaci F, Spoto B, Pisano A, Sanguedolce MC, Tripepi G, Leonardis D, Zoccali C. Association of a polymorphism in a gene encoding a urate transporter with CKD progression. Clin J Am Soc Nephrol. 2014;9(6):1059–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Dalbeth N, Merriman TR, Stamp LK. Gout. Lancet (London, England). 2016;388(10055):2039–52.

    Article  CAS  Google Scholar 

  14. Richette P, Doherty M, Pascual E, Barskova V, Becce F, Castaneda-Sanabria J, Coyfish M, Guillo S, Jansen TL, Janssens H, et al. 2016 updated EULAR evidence-based recommendations for the management of gout. Ann Rheum Dis. 2017;76(1):29–42.

    Article  CAS  PubMed  Google Scholar 

  15. Kodithuwakku ND, Pan M, Zhu YL, Zhang YY, Feng YD, Fang WR, Li YM. Anti-inflammatory and antinociceptive effects of Chinese medicine SQ gout capsules and its modulation of pro-inflammatory cytokines focusing on gout arthritis. J Ethnopharmacol. 2013;150(3):1071–9.

    Article  PubMed  Google Scholar 

  16. Shi L, Zhao F, Zhu F, Liang Y, Yang F, Zhang G, Xu L, Yin L. Traditional Chinese Medicine Formula “Xiaofeng granules” suppressed gouty arthritis animal models and inhibited the proteoglycan degradation on chondrocytes induced by monosodium urate. J Ethnopharmacol. 2016;191:254–63.

    Article  PubMed  Google Scholar 

  17. Al-Okbi SY, Mohamed DA, Hamed TE, Esmail R, Donya SM. Prevention of renal dysfunction by nutraceuticals prepared from oil rich plant foods. Asian Pac J Trop Biomed. 2014;4(8):618–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Yin L, Si X. Ideas and Methods of Screening Jiaweisimiaowan for Treatment of Acute Gouty Arthritis and Principles of Diagnosis. Modern Trad Chin Med Materia Medica-World Sci Technol. 2006;8(5):27–30.

    Google Scholar 

  19. Liu M. Clinical study of modified simiao powder in the treatment of hyperuricemia and acute gout arthritis. Liaoning J Tradi Chin Med. 2011;38(04):675–7.

    Google Scholar 

  20. Shi L, Xu L, Yang Y, Song H, Pan H, Yin L. Suppressive effect of modified Simiaowan on experimental gouty arthritis: An in vivo and in vitro study. J Ethnopharmacol. 2013;150(3):1038–44.

    Article  PubMed  Google Scholar 

  21. Kong LD, Cai Y, Huang WW, Cheng CH, Tan RX. Inhibition of xanthine oxidase by some Chinese medicinal plants used to treat gout. J Ethnopharmacol. 2000;73(1–2):199–207.

    Article  CAS  PubMed  Google Scholar 

  22. Zhao F, Guochun L, Yang Y, Shi L, Xu L, Yin L. A network pharmacology approach to determine active ingredients and rationality of herb combinations of Modified-Simiaowan for treatment of gout. J Ethnopharmacol. 2015;168:1–16.

    Article  CAS  PubMed  Google Scholar 

  23. Liang X, Li H, Li S. A novel network pharmacology approach to analyse traditional herbal formulae: the Liu-Wei-Di-Huang pill as a case study. Mol BioSyst. 2014;10(5):1014–22.

    Article  CAS  PubMed  Google Scholar 

  24. Yu G, Wang W, Wang X, Xu M, Zhang L, Ding L, Guo R, Shi Y. Network pharmacology-based strategy to investigate pharmacological mechanisms of Zuojinwan for treatment of gastritis. BMC Complement Altern Med. 2018;18(1):292.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Yu G, Zhang Y, Ren W, Dong L, Li J, Geng Y, Zhang Y, Li D, Xu H, Yang H. Network pharmacology-based identification of key pharmacological pathways of Yin-Huang-Qing-Fei capsule acting on chronic bronchitis. Int J Chron Obstruct Pulmon Dis. 2017;12:85–94.

    Article  CAS  PubMed  Google Scholar 

  26. Ru J, Li P, Wang J, Zhou W, Li B, Huang C, Li P, Guo Z, Tao W, Yang Y, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Xu HY, Zhang YQ, Liu ZM, Chen T, Lv CY, Tang SH, Zhang XB, Zhang W, Li ZY, Zhou RR, et al. ETCM: an encyclopaedia of traditional Chinese medicine. Nucleic Acids Res. 2019;47(D1):D976-d982.

    Article  CAS  PubMed  Google Scholar 

  28. Liu Z, Guo F, Wang Y, Li C, Zhang X, Li H, Diao L, Gu J, Wang W, Li D, et al. BATMAN-TCM: a Bioinformatics analysis tool for molecular mechanism of traditional Chinese medicine. Sci Rep. 2016;6:21146.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Huang J, Tang H, Cao S, He Y, Feng Y, Wang K, Zheng Q. Molecular Targets and Associated Potential Pathways of Danlu Capsules in Hyperplasia of Mammary Glands Based on Systems Pharmacology. Evid Based Complement Altern Med. 2017;2017:1930598.

    Google Scholar 

  30. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074-d1082.

    Article  CAS  PubMed  Google Scholar 

  31. UniProt Consortium T. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2018;46(5):2699.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Li YH, Yu CY, Li XX, Zhang P, Tang J, Yang Q, Fu T, Zhang X, Cui X, Tu G, et al. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res. 2018;46(D1):D1121-d1127.

    Article  CAS  PubMed  Google Scholar 

  33. Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K, Thorn CF, Altman RB, Klein TE. Pharmacogenomics Knowledge for Personalized Medicine. Clin Pharmacol Ther. 2012;92(4):414–7.

    Article  CAS  PubMed  Google Scholar 

  34. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005;33:D514–7.

    Article  CAS  PubMed  Google Scholar 

  35. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C, Chanda SK. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607-d613.

    Article  CAS  PubMed  Google Scholar 

  37. da Huang W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57.

    Article  CAS  Google Scholar 

  38. Hu QH, Jiao RQ, Wang X, Lv YZ, Kong LD. Simiao pill ameliorates urate underexcretion and renal dysfunction in hyperuricemic mice. J Ethnopharmacol. 2010;128(3):685–92.

    Article  PubMed  Google Scholar 

  39. Deng L, Pang P, Zheng K, Nie J, Xu H, Wu S, Chen J, Chen X: Forsythoside A Controls Influenza A Virus Infection and Improves the Prognosis by Inhibiting Virus Replication in Mice Mol. 2016;21(5):524.

  40. Li P, Wang Y. cao Y: Meta-analysis of the Efficacy and Safety of Simiao Decoction on Gout Compared with Colchicine. J Emerg Trad Chin Med. 2019;28(3):449–52.

    Google Scholar 

  41. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Dalbeth N, Pool B, Gamble GD, Smith T, Callon KE, McQueen FM, Cornish J. Cellular characterization of the gouty tophus: a quantitative analysis. Arthritis Rheum. 2010;62(5):1549–56.

    Article  CAS  PubMed  Google Scholar 

  43. Dalbeth N, Lauterio TJ, Wolfe HR. Mechanism of action of colchicine in the treatment of gout. Clin Ther. 2014;36(10):1465–79.

    Article  CAS  PubMed  Google Scholar 

  44. Newson J, Motwani MP, Kendall AC, Nicolaou A, Muccioli GG, Alhouayek M, Bennett M, Van De Merwe R, James S, De Maeyer RPH, et al. Inflammatory Resolution Triggers a Prolonged Phase of Immune Suppression through COX-1/mPGES-1-Derived Prostaglandin E2. Cell Rep. 2017;20(13):3162–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wang Y, Liu Y, Zhang M, Lv L, Zhang X, Zhang P, Zhou Y. Inhibition of PTGS1 promotes osteogenic differentiation of adipose-derived stem cells by suppressing NF-kB signaling. Stem Cell Res Ther. 2019;10(1):57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Tian B, Widen SG, Yang J, Wood TG, Kudlicki A, Zhao Y, Brasier AR. The NFkappaB subunit RELA is a master transcriptional regulator of the committed epithelial-mesenchymal transition in airway epithelial cells. J Biol Chem. 2018;293(42):16528–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Nishimoto N. Interleukin-6 in rheumatoid arthritis. Curr Opin Rheumatol. 2006;18(3):277–81.

    Article  CAS  PubMed  Google Scholar 

  48. Bedir A, Topbas M, Tanyeri F, Alvur M, Arik N. Leptin might be a regulator of serum uric acid concentrations in humans. Jpn Heart J. 2003;44(4):527–36.

    Article  CAS  PubMed  Google Scholar 

  49. Emmerson B. Hyperlipidaemia in hyperuricaemia and gout. Ann Rheum Dis. 1998;57(9):509–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Dessein PH, Shipton EA, Stanwix AE, Joffe BI, Ramokgadi J. Beneficial effects of weight loss associated with moderate calorie/carbohydrate restriction, and increased proportional intake of protein and unsaturated fat on serum urate and lipoprotein levels in gout: a pilot study. Ann Rheum Dis. 2000;59(7):539–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Berthelot CC, Kamita SG, Sacchi R, Yang J, Nording ML, Georgi K, Hegedus Karbowski C, German JB, Weiss RH, Hogg RJ, et al. Changes in PTGS1 and ALOX12 Gene Expression in Peripheral Blood Mononuclear Cells Are Associated with Changes in Arachidonic Acid, Oxylipins, and Oxylipin/Fatty Acid Ratios in Response to Omega-3 Fatty Acid Supplementation. PLoS ONE. 2015;10(12):e0144996.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Li X, Mazaleuskaya LL, Ballantyne LL, Meng H, FitzGerald GA, Funk CD. Genomic and lipidomic analyses differentiate the compensatory roles of two COX isoforms during systemic inflammation in mice. J Lipid Res. 2018;59(1):102–12.

    Article  CAS  PubMed  Google Scholar 

  53. Deng J, Guo Y, Yuan F, Chen S, Yin H, Jiang X, Jiao F, Wang F, Ji H, Hu G, et al. Autophagy inhibition prevents glucocorticoid-increased adiposity via suppressing BAT whitening. Autophagy. 2020;16(3):451–65.

  54. Tsuchida A, Yamauchi T, Takekawa S, Hada Y, Ito Y, Maki T, Kadowaki T. Peroxisome proliferator-activated receptor (PPAR)alpha activation increases adiponectin receptors and reduces obesity-related inflammation in adipose tissue: comparison of activation of PPARalpha, PPARgamma, and their combination. Diabetes. 2005;54(12):3358–70.

    Article  CAS  PubMed  Google Scholar 

  55. Cheng Y, Li S, Wang M, Cheng C, Liu R. Peroxisome Proliferator Activated Receptor gamma (PPARgamma) Agonist Rosiglitazone Ameliorate Airway Inflammation by Inhibiting Toll-Like Receptor 2 (TLR2)/Nod-Like Receptor with Pyrin Domain Containing 3 (NLRP3) Inflammatory Corpuscle Activation in Asthmatic Mice. Med Sci Monit. 2018;24:9045–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Wang Q, Zhu G, Cao X, Dong J, Song F, Niu Y. Blocking AGE-RAGE Signaling Improved Functional Disorders of Macrophages in Diabetic Wound. J Diabetes Res. 2017;2017:1428537.

    PubMed  PubMed Central  Google Scholar 

  57. Peng Y, Kim JM, Park HS, Yang A, Islam C, Lakatta EG, Lin L. AGE-RAGE signal generates a specific NF-kappaB RelA “barcode” that directs collagen I expression. Sci Rep. 2016;6:18822.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Juraschek SP, Miller ER 3rd, Gelber AC. Effect of oral vitamin C supplementation on serum uric acid: a meta-analysis of randomized controlled trials. Arthritis Care Res. 2011;63(9):1295–306.

    Article  CAS  Google Scholar 

  59. Fadok VA, Bratton DL, Konowal A, Freed PW, Westcott JY, Henson PM. Macrophages that have ingested apoptotic cells in vitro inhibit proinflammatory cytokine production through autocrine/paracrine mechanisms involving TGF-beta, PGE2, and PAF. J Clin Investig. 1998;101(4):890–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Busso N, Ea HK. The mechanisms of inflammation in gout and pseudogout (CPP-induced arthritis). Reumatismo. 2012;63(4):230–7.

    Article  CAS  PubMed  Google Scholar 

  61. Cronstein BN, Sunkureddi P. Mechanistic Aspects of Inflammation and Clinical Management of Inflammation in Acute Gouty Arthritis. Jcr-J Clin Rheumatol. 2013;19(1):19–29.

    Article  Google Scholar 

  62. Peng X, Li J, Tan S, Xu M, Tao J, Jiang J, Liu H, Wu B. COX-1/PGE2/EP4 alleviates mucosal injury by upregulating beta-arr1-mediated Akt signaling in colitis. Sci Rep. 2017;7(1):1055.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Schaefer S, Guerra B. Protein kinase CK2 regulates redox homeostasis through NF-kappaB and Bcl-xL in cardiomyoblasts. Mol Cell Biochem. 2017;436(1–2):137–50.

    Article  CAS  PubMed  Google Scholar 

  64. Yin J, Sheng B, Qiu Y, Yang K, Xiao W, Yang H. Role of AhR in positive regulation of cell proliferation and survival. Cell Prolif. 2016;49(5):554–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Tian L, Li Y, Wang C, Li Q. Let-7g-5p regulates mouse mammary cells differentiation and function by targeting PRKCA. J Cell Physiol. 2019;234(7):10101–10.

    Article  CAS  PubMed  Google Scholar 

  66. Gao J, Ailifeire M, Wang C, Luo L, Zhang J, Yuan L, Zhang L, Li X, Wang M: miR-320/VEGFA axis affects high glucose-induced metabolic memory during human umbilical vein endothelial cell dysfunction in diabetes pathology. Microvasc Res. 2020;127:103913.

  67. Gurule NJ, Heasley LE. Linking tyrosine kinase inhibitor-mediated inflammation with normal epithelial cell homeostasis and tumor therapeutic responses. Cancer Drug Resist (Alhambra, Calif). 2018;1:118–25.

    Google Scholar 

  68. Zhu Y, Pandya BJ, Choi HK. Comorbidities of gout and hyperuricemia in the US general population: NHANES 2007–2008. Am J Med. 2012;125(7):679-687.e671.

    Article  PubMed  Google Scholar 

  69. Mallamaci F, Testa A, Leonardis D, Tripepi R, Pisano A, Spoto B, Sanguedolce MC, Parlongo RM, Tripepi G, Zoccali C. A genetic marker of uric acid level, carotid atherosclerosis, and arterial stiffness: a family-based study. Am J Kidney Dis. 2015;65(2):294–302.

    Article  CAS  PubMed  Google Scholar 

  70. He D, Huang JH, Zhang ZY, Du Q, Peng WJ, Yu R, Zhang SF, Zhang SH, Qin YH. A Network Pharmacology-Based Strategy For Predicting Active Ingredients And Potential Targets Of LiuWei DiHuang Pill In Treating Type 2 Diabetes Mellitus. Drug Des Devel Ther. 2019;13:3989–4005.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Lee S, Youn K, Jun M. Major compounds of red ginseng oil attenuate Aβ(25–35)-induced neuronal apoptosis and inflammation by modulating MAPK/NF-κB pathway. Food Funct. 2018;9(8):4122–34.

    Article  CAS  PubMed  Google Scholar 

  72. Zhao H, Zhang X, Wang M, Lin Y, Zhou S. Stigmasterol Simultaneously Induces Apoptosis and Protective Autophagy by Inhibiting Akt/mTOR Pathway in Gastric Cancer Cells. Front Oncol. 2021;11:629008.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Zhou BX, Li J, Liang XL, Pan XP, Hao YB, Xie PF, Jiang HM, Yang ZF, Zhong NS. β-sitosterol ameliorates influenza A virus-induced proinflammatory response and acute lung injury in mice by disrupting the cross-talk between RIG-I and IFN/STAT signaling. Acta Pharmacol Sin. 2020;41(9):1178–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Niyukuri J, Raiti J, Ntakarutimana V, Hafidi A. Lipid composition and antioxidant activities of some underused wild plants seeds from Burundi. Food Sci Nutr. 2021;9(1):111–22.

    Article  CAS  PubMed  Google Scholar 

  75. Vo TK, Ta QTH, Chu QT, Nguyen TT, Vo VG: Anti-Hepatocellular-Cancer Activity Exerted by β-Sitosterol and β-Sitosterol-Glucoside from Indigofera zollingeriana Miq. Mol. 2020, 25(13):3021.

Download references

Acknowledgements

We are grateful to Medical experimental center, School of Medicine, Jinan University. The data of KEGG pathway analysis in the research comes from the KEGG database (https://www.kegg.jp/kegg/kegg1.html).

Funding

This study was supported by the National Natural Science Foundation of China (No. 81830114 and 82174253 and 82104707), the Natural Science Foundation of Guangdong, China (No.2020A1515010756), Key-Area Research and Development Program of Guangdong Province (No.2020B1111100010) and China Postdoctoral Science Foundation (No.2020M683206).

Author information

Author notes

  1. Huachong Xu and Jialin Wu contributed equally to this work.

Authors and Affiliations

  1. College of Traditional Chinese Medicine, Jinan University, Guangzhou, 510632, China

    Huachong Xu, Jialin Wu, Shiqi Wang, Lu Xu, Pei Liu, Yucong Shi, Sizhi Wu, Li Deng & Xiaoyin Chen

Authors
  1. Huachong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jialin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shiqi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lu Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yucong Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sizhi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Li Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xiaoyin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

(I) Conception and design: L Deng, X Chen, H Xu; (II) Administrative support: L Deng, X Chen; (III) Provision of study materials or patients: J Wu, P Liu, Y Shi; (IV) Collection and assembly of data: H Xu, S Wu; (V) Data analysis and interpretation: H Xu, J Wu, P Liu; (VI) Animal experiments were mainly conducted by J Wu, S Wang and L Xu; (VII)Manuscript writing: All authors; (VIII) Final approval of manuscript: All authors. Huachong Xu and Jialin Wu contributed equally to this work.

Corresponding authors

Correspondence to Li Deng or Xiaoyin Chen.

Ethics declarations

Ethics approval and consent to participate

The protocol established in this research was approved by the Institutional Animal Care Committee of Jinan University (Guangzhou, China), Approval Number: 20200731-02. All experimental process were carried out in accordance with relevant guidelines and regulations. The study was performed in compliance with ARRIVE guidelines.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, H., Wu, J., Wang, S. et al. Network pharmacology and in vivo experiments reveal the pharmacological effects and molecular mechanisms of Simiao Powder in prevention and treatment for gout. BMC Complement Med Ther 22, 152 (2022). https://doi.org/10.1186/s12906-022-03622-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12906-022-03622-0

Keywords

BMC Complementary Medicine and Therapies

ISSN: 2662-7671

Contact us