A Network Pharmacology Exploring the Mechanism of a Chinese Medicine Pair for the Treatment of Gout

by on the disturbance of purine excretory and/or hyperuricemia, and In Dioscoreae Hypoglaucae Rhizoma (DH) and Smilacis Glabrae Rhixoma (SG) are widely used as medicinal pairs to prevent and treat gout. However, there is still a lack of research on the mechanism of DS (DH and SG). The aim of this study is to identify the absorbable components, potential targets and related therapeutic pathways of DS by means of network pharmacology. In this study, we tried to explain the mechanism of DS in the prevention and treatment of gout by using the network pharmacological method and provided an alternative way to study the effect of this Chinese medicine pair.

In this study, we collected the effective therapeutic targets of gout, as well as the composition and corresponding therapeutic targets of this drug pair. In order to reveal the rationality of DS, we established the topological network analysis of TCM components and targets of DS, which provided a potential synergistic mechanism for the treatment of gout by DS. The biological network analytical approach applied in the research may be signi cant for the exploring of functionary effect of TCM.

Building database of ingredients of DS
All the chemical ingredients' data of DH and SG were downloaded from Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) (http://lsp.nwu.edu.cn/tcmsp.php) (Yu et al. 2020). The TCMSP database provides pharmacokinetic information for each compound. Therefore, users can select compounds with analogous characteristics to drugs and ADME (absorption, distribution, metabolism, excretion) for further research (Nielsen et al. 2017).

Screening of active compounds
Through the integration of oral bioavailability (OB) and drug-likeness (DL), the active components of DH and SG were screened. DL is useful in describing the pharmacokinetics and peculiarities of compounds, such as solvability and chemical stability. Usually, the inclusion criteria of "drug-like" compounds in TCM is 0.18 (Luo et al. 2020). OB is the ratio of oral drugs absorbed into blood circulation. Because the low OB level is the main cause for the transformation of TCM into curative medicine, it is very important to carry out OB screening criteria. According to the literature and suggestions, the OB ≥ 30% and DL ≥ 0.18 of the compounds were set as screening conditions to screen the compounds with high activity in TCMSP (Du et al. 2017). The ingredients meeting above-mentioned both criteria will be conserved for further study.

TCM-associated target prediction
Combined with three databases, the related indicators of active ingredients in DH and SG were synthetically predicted. GeneCards database (Wang et (Chen et al. 2018), the keywords "gout" and "hyperuricemia" were retrieved. Also, the related literatures were searched for the reported gout-related genes. After removing duplicate and false positive genes, 98 gout-related target genes were nally collected.

Construction of protein-protein interaction (PPI) network
In order to illustrate the role of target proteins at the system level, Bisogenet plug-ins in Cytoscape 3.5.1 software (http://www.cytoscape.org/) (Cox et al. 2018) were used to construct component target PPI and disease target PPI, respectively. Topological analysis of them and screening of core targets were carried out.

Network construction and node screening
The different targets from DH, SG and gout were submitted to Agilent Literature Search Software 2.78 (Liu et al. 2015). Based on the human targets, we set "Max Engine Matches" as 10 and researched in the full text.
After that, the protein-protein interaction meshwork was visualized by Cytoscape 3.5.1 software.

Main active components of DS
A total of 85 active ingredients in DS were collected from TCMP database, including 75 ingredients of DH and 10 ingredients of SG. In this study, 16 active compounds from 85 compounds met both the criteria, OB ≥ 30% and DL ≥ 0.18 (Table 1

Complex network construction and analysis
On the basis of the data in Table 1, network is built. According to existing proof, we get that DS's traditional effects have something to do with contemporary pharmacology, which illustrates that some targets are hit by diverse compounds. Therefore, we can roughly understand the relationship between the drug active compounds and the action targets from the target network. This shows that DS's compounds may affect on these targets in phase and thus take therapeutic effect in else besides gout, which invisibly show DS's feature of multicompound-multitarget-multidisease. Its possible functions may be explored by the network (Fig. 1).

KEGG pathway enrichment analysis
KEGG pathway enrichment analysis of common core target proteins screened by DS was carried out using DAVID database, 90.6% of which were analyzed, and 97 signaling pathways were obtained. Screening was performed with P values < 0.01 and FDR < 0.05. The results of 24 pathways are described in Table 2, which indicate that the active ingredients of DS may be used to treat gout by acting on these signaling pathways.
Omicshare database was used to visualize the results of enrichment analysis (Fig. 2). On the basis of KEGG analysis result, these targets are very much related to the Neuroactive ligand-receptor interaction (Fold Enrichment = 6; P < 0.001), Calcium signaling pathway (Fold Enrichment = 5.4; P < 0.001), Morphine addiction (Fold Enrichment = 7.9; P < 0.001), Nicotine addiction (Fold Enrichment = 11.1; P < 0.001) and so on. This suggests that DS can regulate many metabolic signaling pathways and play a considerable regulatory part in the pathophysiological processes of metabolic and immune reaction.

Network construction and node screening
Network formation was automatically completed through retrieval by Agilent Literature Retrieval. The topological parameters of DH, SG and gout show that node-degree distribution submits to the power law distribution. As displayed in Fig. 3, the drug pair shares 9 targets (NLRP3, STS, EPHB2, PRKAA1, ROS1, SLC22A12, ATP8A2, IRF6 and P2RX7) with gout.

Discussion
Gout is a kind of hyperuricemia caused by the increased synthesis or decreased excretion of uric acid due to Hyperuricemia increases ROS1 production through activation of NADPH oxidase and xanthine oxidase (Roskoski 2017

Conclusion
In this study, in order to better identify the drug e cacy of DS, we conducted integrated component prediction and DS target pathway analysis strategy using network pharmacology. The method mins and extracts the potential synergy effect of DH and SG from the perspective of interaction network. This study found that gout and DS share common targets such as NLRP3, STS, EPHB2, PRKAA1, ROS1, SLC22A12, ATP8A2, IRF6 and P2RX7, which may be the key points for DS to treat gout. But clinical and experimental trials require further validation.

Data Availability
The data used to support this study are included within this article.

Disclosure
Wei Liu, and Wenjia Zhao are the co-rst authors.

Competing Interests
The authors declare that there is no con ict of interests regarding the publication of this paper.

Authors' Contributions
Liu Wei is responsible for the study concept, design, and literature searching; Liu Wei and Wu Yuan-hao are responsible for data analysis and interpretation; Wu Yuan-hao and Zhao Wen-jia carried out extensive revision of the manuscript; all authors participated in the analysis and interpretation of data and approved the nal paper. The authors declare that all data were generated in-house and that no paper mill was used.