Study on The Effect Mechanism of “Common Treatment for Different Diseases” of Dachaihu Decoction on Prediabetes and Acute Hemorrhagic Stroke Based on Network Pharmacology


 Background: In this study, network pharmacology method was used to systematically predict and analyze the mechanism of "Common treatment for different diseases" effect of Dachaihu Decoction(DCHD) in the treatment of Prediabetes(PD) and Acute hemorrhagic stroke(AHS).Methods: TCMsp (Traditional Chinese Medicine systems pharmacology database and analysis platform) database was used to collect all the candidate active components related to 8 kinds of traditional Chinese medicine of DCHD, and UniProt database was used to obtain the drug action target and construct the "traditional Chinese medicine -Compound -target" action network; Genecards, OMIM(Online Mendelian Inheritance in Man), DisGeNET, CTD(Comparative Toxicogenomics Database) and TTD（Therapeutic Target Database）databases were used to obtain the related genes of PD and AHS respectively, and the interaction analysis of Venn with potential active components was carried out to obtain the common target of DCHD in the treatment of the two diseases.Using STRING 11.0 and Cytoscape3.72 to analyze protein-protein interaction of common targets and screen key common targets. BioGPS was used to obtain the distribution information in organs and tissues, and the relationship between the molecules and the key functional molecules were described. Bioconductor (R) was used to analyze the gene ontology (go) enrichment and the pathway analysis of the Kyoto Encyclopedia of genes and genomes (KEGG), so as to systematically predict the mechanism of "Common treatment for different diseases" of DCHD for PD and AHS.Results: with OB ≥ 30% and DL ≥ 0.18 as the screening criteria, 133 active compounds were screened out and 1034 drug targets were obtained; There are 3878 PD gene targets, 2674 AHS gene targets, 129 drug disease common targets, and 10 key targets whose median value is greater than 18;The key common targets displayed by biogps are mainly distributed in CD33+_ Myeloid.2(degree = 4)，Prostate.2(degree = 3)，CD56+_ NKCells.1(degree = 3)，Lung.2(degree = 3)，CD56+_ Nkcells. 2 (degree = 2);2281 biological processes, 65 cell components and 142 molecular functions were obtained by GO functional enrichment analysis;161 signal pathways were obtained by KEGG enrichment analysis, and the ones with higher proportion were AGE-RAGE signaling pathway in diabetic complications,PI3K-Akt signaling pathway,TNF signaling pathway,IL-17 signaling pathway,MAPK signaling pathway,HIF-1 signaling pathway,Relaxin signaling pathwa,C-type lectin receptor signaling pathway,which is mainly related to oxidative stress, glycolipid metabolism, immune inflammatory response, and neuroendocrine.Conclusion: DCHD can achieve the effect of "Common treatment for different diseases" by acting on the common receptor of PD and AHS through multi-component, multi-target and multi-channel, providing reference for further experimental verification, potential pharmacological mechanism and clinical application.

rhubarb, can reduce the permeability of pancreatic cells by promoting the expression of tight junction protein-5 and closure protein [25] ;Scutellarin and Baicalin in Scutellaria baicalensis Georgi can clear many kinds of free radicals, inhibit the metabolism of xanthine oxidase to produce oxygen free radicals, and be used to treat diseases related to free radicals and oxidative stress. They are effective antioxidants in Scutellaria baicalensis Georgi; [26] Paeony can inhibit platelet aggregation and compatibility of Bupleurum can reduce blood viscosity [27] ,Fructus aurantii Immaturus can promote the decomposition of fat, reduce blood lipid, and has the function of antioxidation and scavenging free radicals [28] .At the same time, by regulating the in ammatory reaction, DCHD can signi cantly reduce the levels of TNF -α, IL-6 and IL-8 in patients' serum. Meanwhile, regulating the oxidative reaction can increase the activity of SOD and CAT, and reduce the content of MDA [29] And enhance the immune response,So as to achieve the purpose of regulating PD and preventing and treating AHS.
Although the clinical effect of DCHD is accurate at present, for PD and AHS, "Common treatment for different diseasesr" may be achieved through "brain gut axis". However, due to the characteristics of "multi-component, multi-target, multi-channel" of traditional Chinese medicine compound, the speci c mechanism of action is still unclear. It is di cult to systematically and completely elucidate its mechanism of action only by traditional pharmacological research methods, Network pharmacology integrates the ideas of system biology and multi-directional pharmacology. By building the interaction relationship between "drugs-target-disease", it combines multi-disciplinary and multi-group databases to apply to the screening of material basis, the identi cation of new drug targets and the study of action mechanism. The research strategy of network pharmacology integrity is consistent with the concept of TCM integrity. [30][31] In this study, the network pharmacology method was used to systematically explore the mechanism of DCHD in the treatment of PD and AHS from the perspective of "Common treatment for different diseases", so as to provide a reference for its in-depth study and clinical application.The owchart of the experimental procedures of our study is shown in Fig. 1 In our study, (OB) ≥30% and drug likeness (DL) ≥0.18 were selected as candidate active components using the TCMSP database, and their potential targets were retrieved.

Identi cation of gene names.
The screened effective targets (the duplicates were removed) were combined with similar terms to obtain the potential effective targets of all herbs in DCHD. Next, we used the UniProtKB search function in the UniProt database (UniProt; http://www.uniprot.org/) to obtain the o cial symbol for each protein by inputting the protein names with the species limited to "Homo sapiens". Then, we can get the right genetic symbols.

Network construction
To visualize the complex interactions between components and potential targets, we established the complex networks by using Cytoscape (Cytoscape; http://cytoscape.org/, ver.3.7.2). In this study, network pharmacology was used to explore the interrelationships of the herbs, their ingredients, and targets with PD and AHS, which were represented by nodes and edges. The network construction was performed as follows:(1) Venn diagram of drug targets with the genes associated with a disease;(2) the active compounds-active compounds target network of DCHD was built; (3) the herb-compound target-disease target network was built via linking the eight DCHD herbs with compound targets of each herb, and disease targets.
We constructed the networks by utilizing the network visualization software Cytoscape. In this network, there are three important indices including degree, node betweenness, and closeness to evaluate every node. At rst, Degree indicates the number of edges between a node and other nodes in a network. [32] Secoundly, Node betweenness evaluates the participation of a node in the shortest parts of the network and re ects the ability of node stop proceed the rate of information ow in the network as well. [33] At last, Closeness refers to the inverse of the sum of the distance from a node to other nodes.This indicates determine whether a target protein important basis for the key targets. [34] 2.3 Protein-Protein Interaction Data The PPI network was constructed using the STRING platform(https://string-db.org/), and the species was set as "Homo sapiens". The minimum threshold of interaction was set as "high con dence data > 0.7".
Meanwhile, the PPI network was constructed via utilizing the network visualization software Cytoscape3.7.2. It is important to explore protein interactions and their interaction networks to understand cellular organization, bioprocesses, and functions.

Enrichment of Gene Ontology (GO)Pathway and the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway
GO is an international standard classi cation system for gene function. GO enrichment analyses can reveal the functional changes in these targets in three respects: molecular biological function, biological process, and cellular components. And then KEGG metabolic pathway enrichment analysis was carried out to study the main metabolic pathway of YQQDC in the treatment of COVID-19. We used Bioconductor (R)v3.6.2 (Bioconductor (R); http://bioconductor.org/)for analyses. The statistics were collected by the ClueGO and CluePedia plugins with the FDR set as ≤ 0.05.  (Tables S1-S8). Simultaneously, We screened 3878 important gene targets related to PD and 2674 important gene targets related to AHS (Table S9-S10). In other words, 129 genetic symbols may be the key to DCHD 's treatment of PD and AHS.

Drug Target -Disease Target Analysis
We through the analysis of Venn's diagram can get the conclusion that there are 129 overlaps between 3878 PD related gene targets, 2674 AHS related gene targets and 1034 effective disease targets. (Figure2)

Drug Ingredients-Target Network Analysis
The drug composition target network consists of 293 nodes (80 compounds and 206 compound targets in DCFT) and 785 edges.As shown in Figure 3, A1 (kaempferol) is CH, and BS has common components;A2 (quercetin) is a common component of CH and DZ;A3 (stigmasterol) is a common component of HQ, BX, SJ, DZ;A4 (baicalein) is a common component of HQ, BX ;A5 (eriodyctiol ( avanone)) is HQ and ZS;A6 (beta sitosterol) is composed of DH, BS, BX and SJ;A7 (sitosterol) is HQ and BS;A8 ((-) -catechin) is the common component of DH, DZ;A9 (mairin,(+) -catechin) which is a common component of BS and DZ;The network shows that many compound targets can be adjusted by a variety of compounds. In addition, we can also roughly observe the relationship between the active compounds and their targets.

Herb Compounds -disease Target-disease Network Analysis
This network was built to show the relationship between eight herbs, compound, PD and AHS targets, PD and AHS( Figure 4). Analysis of this network revealed that the network map contained 201 nodes and 898 edges. The 201 nodes included 69 active components of DCHD, 8 herbs, 2 disease and 129 targets of PD and AHS targets. Figure 3 shows that DCHD may affect drug targets by controlling related proteins (compound targets).

Analyses of a PPI Network
Through the results of Venn graph, we get 129 repeat important targets. In order to illuminate the signi cance of degree in compound targets, we created a PPI network about the relationship of the common targets between compounds and PD and AHS. ( Figure 5) This PPI network consisting of 129 nodes and 492 edges. Meanwhile, the PPI network was constructed via utilizing the network visualization software Cytoscape3.7.2. (Figure 5) In this network, the node size and color are used to reflect the number of combined targets (degree). As shown in Figure 6, the darker color and the larger circle indicates a higher degree. What's more, the bar plot of the PPI network analysis results showed that were the pivotal targets in this network. (Figure 7) In this gure, the x-axis represents the number of neighboring proteins of the target protein. The y-axis represents the target protein. From the above gures, we indicating that there were 10 core targets obtained after PPI network analysis, as well as the critical role in the treatment of PD and AHS.

Network construction and topological analysis of "key target organ tissue"
Key shared targets AKT1, Jun, rela, IL6, mapk1, app, Mapk14, EGFR, Mapk8, VEGFA were imported into biogps to obtain the distribution in organs and tissues, and relevant information was imported into Cytoscape 3.72 to build a "key target organ tissue" network map and carry out topological analysis( Figure  8) . In the gure, the blue diamond represents the disease, and the pink triangle node is the key target, The red circle node is the organ tissue, and the circle size represents the size of the degree value. According to the degree value, the information of the rst ve distribution organs is as follows: CD33+_ Myeloid.2(degree = 4) Prostate.2(degree = 3) CD56+_ NKCells.1(degree = 3) Lung.2(degree = 3) CD56+_NKCells.2 (degree = 2) suggesting that the above organs and tissues play an important role in the treatment of PD and AHS.

Analyses of Enrichment of GO Pathways
Using the GO enrichment analysis function of the Bioconductor (R), We carried out GO enrichment analyses to further determine the functions of these shared targets from three aspects, and GO entries were determined using a false discovery rate (FDR) of <0.05. Through the analysis of GOBP, GOCC, GOMF, we obtained 2281 biological processes, 65 cell components and 142 molecular functions. As shown in Figure 9(a,b,c), Top 20 functional terms were enriched in the biological process category, such as response to lipopolysaccharide,response to oxidative stress,response to nutrient levels,reactive oxygen species metabolic process,response to steroid hormone,regulation of apoptotic signaling pathway and muscle cell proliferation.Top 20 functional terms were enriched in the cellular components'category, such as membrane raft,cytoplasmic vesicle lumen,secretory granule lumen, mitochondrial outer membrane,nuclear chromatin,platelet alpha granule, endoplasmic reticulum lumen,protein kinase complex,RNA polymerase II, transcription factor complex and serine/threonine protein kinase complex. Additionally, Top 20 functional terms were enriched in the molecular function category, such as cytokine activity,heme binding,cytokine receptor bindingtetrapyrrole binding,nuclear receptor activity,transcription factor activity, and direct ligand regulated sequence-speci c DNA binding. Undoubtedly, these biological processes were all involved in the pathogenesis of PD and AHS, so they may serve as a potential therapeutic mechanism for PD and AHS.

Analyses of Enrichment of KEGG Pathways
Analyses of enrichment of the KEGG pathway were also carried out using Bioconductor (R) and ClueGO. (p< 0.01) The pathways of 129 proteins involved in PPI network were analyzed via pathway enrichment, and 161signaling pathways were obtained. Figure 10 shows the top 20 pathways for DCHD in the treatment of PD and AHS target. The functionally grouped network of enriched categories was generated for the target genes using ClueGO and CluePedia ( Figure 11). In addition, we established a drug-targetpath network which clearly indicates that DCHD may achieve its purpose of treating PD and AHS through multiple targets and pathways. Figure 13 shows the proportion of each group associated with 129 targets.  Figure 13 shows the AGE-RAGE signaling pathway in diabetic complications ,PI3K-Akt signaling pathway,IL-17 signaling pathway and MAPK signaling pathway were discussed to illustrate the underlying therapeutic mechanisms of DCHD for MAPK signaling pathway treatment.

Discussion
The composition of traditional Chinese medicine compound is complex, in which the quantity and dosage of traditional Chinese medicine are slightly increased or decreased, which can achieve different clinical effects, which is very delicate.Many traditional Chinese medicine compounds can achieve the effect of "Common treatment for different diseases", but the pharmacophore and mechanism of action need to be studied.
The classic prescription DCHD, originated from the treatise on febrile diseases, has the effect of reconciling Shaoyang, clearing away Yang and clearing away Yang Ming. It is widely used in clinical practice. [35] Modern pharmacology has proved that Saikosaponin-d in Bupleurum can not only effectively inhibit a variety of in ammatory reactions, but also inhibit the elevation of blood lipid level; Scutellaria has the effects of cholagogic, liver protecting and blood lipid reducing, and baicalin and baicalin can interfere with arachidonic acid metabolic pathway and inhibit the activity of cytokines, produce antipyretic and anti-in ammatory effects; rhubarb has the effects of reducing serum cholesterol, liver protecting and blood pressure reducing; Paeony has the functions of protecting hepatocytes, promoting hepatocyte regeneration, reducing lipid and inhibiting hepatocyte brosis. [36] At present, DCHD is mainly used in the disease of "Shaoyang Yangming combined disease". [35] Through clinical practice, it is found that DCHD is not only widely used in diabetes, hepatobiliary disease, but also has certain curative effect in the treatment of acute hemorrhagic stroke related diseases. [37] Traditional Chinese medicine is guided by the principle of holistic view and syndrome differentiation and treatment, Network pharmacology is based on the theory of system biology, through the construction of interaction network among drugs, diseases and targets, and then systematically and integrally study the action mechanism of drugs with multiple targets and channels. They have common points in philosophical thinking. [36][37][38] Therefore, this paper uses the method of network pharmacology to explore the "Common treatment for different diseases" PD and AHS of DCHD Mechanism of action.
In this study, through network analysis, we found that DCHD has 129 common targets in the treatment of PD and AHS, and further extracted 10 key common targets by protein interaction network analysis. According to the topological index degree in network analysis, AKT1, JUN, RELA, IL6, MAPK1, APP, MAPK14, EGFR,MAPK8, VEGFA, which are closely related to oxidative stress, glycolipid metabolism, immune in ammatory response, and neuroendocrine regulation are the key common targets, AKT1 (aktserine / threoninekinase1) is one of the three serine / threonine protein kinases (AKT1, AKT2, Akt3) [39]. It regulates the biological processes by participating in multiple signaling pathways related to in ammation, immunity, metabolism and cell proliferation, such as AGE-RAGE, FOXO, EGFR tyrosine kinase inhibitor resistance, PI3K Akt and MAPK. Interleukin (IL) is one of the key common targets, including IL2, IL6, IL10 [40] . IL6 is produced by monocyte macrophages, Th2 cells (thelper2cell), vascular endothelial cells, etc. nitric oxide synthetase 2 (NOS2) can promote IL6 synthesis and participate in in ammatory response through the production of nitric oxide as a kind of messenger molecule In addition, IL-2 is produced by T cells, IL-10 is produced by Th2 cells and monocyte macrophages, all of which are involved in immune response by activating T cells, promoting B cell proliferation and secreting antibodies, activating macrophages, etc The regulation of disease and in ammation. Mapk1, mapk3 and Mapk14 in mitogen activated protein kinase (MAPK) family are another kind of key common targets [41] . MAPK family widely exists in various mammalian tissues. MAPK family can be activated by extracellular stimulation, such as cytokines, neurotransmitters, hormones, cell stress and cell adhesion. Through threestage kinase mode, MAPK family can jointly regulate anti-in ammatory effect There are many important physiological and pathological processes such as cell growth, differentiation, stress adaptation and so on [42] .Epidermal growth factor receptor (EGFR) binds to ligands and activates multiple signal cascades to convert extracellular signals into appropriate cellular responses to activate major downstream signaling pathways such as rasraf-mek-erk, PI3K-Akt [43] .Vascular endothelial growth factor A (VEGFA) promotes blood ow and glucose metabolism by promoting endothelial cell proliferation, migration and inhibiting apoptosis [44] .Tumor necrosis factor (TNF) can not only regulate immune function, but also affect blood vessels, blood ow and blood glucose [45] .
Through the go enrichment analysis function of biological conductor (R), we found that DCHD plays an important role in the treatment of PD and AHS, including the response to lipopolysaccharide, the response to oxidative stress, the response to nutritional level, the process of active oxygen metabolism, the response to steroid hormones , regulation and diffusion of apoptosis signaling pathway and muscle cells, etc. The molecular functions play an important role, such as cytokine activity, heme binding, cytokine receptor binding tetrapyrrole binding, nuclear receptor activity, transcription factor activity and direct ligand regulated sequence speci c DNA binding, MAP kinase activity and so on, which are related to the predicted signal pathway.
In order to further explore the common key signal pathways, we used the biological conductor (R) and cluego to enrich the KEGG signal pathway, and found that the higher signal pathways were AGE-RAGE signaling pathway indiabetic complications, PI3K aktsignaling pathway, TNF signaling pathway, IL-17 signaling pathway, MAPK signaling pathway, HIF-1 signaling pathway, Relaxin signaling pathway, C-type lectin receptor signaling pathway, etc., are mainly related to oxidative stress, metabolism, immune in ammation and neuroendocrine. AGE-RAGE signaling pathway can not only cause glycolipid metabolism disorder and oxidative stress enhancement, but also induce MAPK, JAK-STAT and PI3K Akt signaling pathway to promote in ammation and promote the development of diabetes [46] ,Previous studies also found that regulating AGE-RAGE signaling pathway can alleviate oxidative stress in prefrontal cortex and hippocampus of depressed rats and improve stroke symptoms [47] ,It has been reported that DCHD can reduce the content of oxygen free radicals (such as no, Co, MDA) and improve the level of SOD [48] .It is also con rmed that Th17 and IL-17 signaling pathway affect diabetes mellitus and acute hemorrhagic stroke by regulating immune in ammatory response. [49][50][51][52] It is also proved that DCHD can reduce the levels of hs CRP, IL-6 and TNF -α in serum, and reduce the damage of in ammatory factors to the focus and surrounding cells. [53,54] The common signaling pathways regulating immune and in ammatory responses in PD and AHS also include EGFR tyrosine kinase inhibitor resistance, TNF, PI3K Akt, toll like receptor, MAPK, JAK stat, cytokine cytokine receptor interaction and other signaling pathways [55][56][57][58][59][60] .HIF-1 signaling pathway has cell-speci c response to vascular endothelial cells, smooth muscle cells and macrophages. By up regulating VEGF, NO, ROS and PDGF, it can cause endothelial cell dysfunction, angiogenesis and in ammatory response, and affect glucose metabolism and blood oxygen content, [61] On the other hand, DCHD can reduce blood viscosity and brinogen level, inhibit platelet aggregation, promote thrombolysis, correct abnormal Hemorheology and increase effective circulation perfusion. The results are con rmed by each other. [62] In order to further predict the possible organs / tissues of DCHD, it was found that the key common targets were mainly distributed in CD33 through the analysis of biogps+_ Myeloid.2 (degree=4) Prostate.2(degree=3) CD56+_ NKCells.1 (degree=3) Lung.2(degree=3) CD56+_ Nkcells. 2 (degree = 2), which is closely related to the metabolism of glycolipid energy, the proliferation and migration of vascular smooth muscle cells, the active oxygen metabolism process involved in acute hemorrhagic stroke, the response to steroid hormones, the regulation of apoptosis signal pathway and muscle cells, in a certain extent re ects the reliability of the network construction method The following experimental veri cation provides the basis.

Conclusions
To sum up, through the prediction of network pharmacology, it is found that DCHD may through the common signal pathway to regulate oxidative stress, glycolipid metabolism, immune in ammatory response and neuroendocrine regulation to achieve the "Common treatment for different diseases" for PD and AHS, which is consistent with the current research on the mechanism of PD and AHS to some extent, it indicates the reliability and accuracy of network pharmacology prediction results, but further experimental veri cation is needed to clarify the in uence of DCHD on the above prediction key targets and signal pathways. This study shows that DCHD has the characteristics of multi-component, multitarget and multi-channel, which can provide a reference for the next stage to study the mechanism of DCHD, and also provide a basis for the rational clinical application and new drug development.  Figure 1 Work ow for Dachaihu Decoction against Acute hemorrhagic stroke based on network pharmacology

Figure 2
The 129 matching targets of the related targets in DCHD on PD and AHS.