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Table of Contents
ORIGINAL ARTICLE
Year : 2018  |  Volume : 4  |  Issue : 4  |  Page : 147-162

A network pharmacology approach to decipher the mechanisms of anti-depression of Xiaoyaosan formula


1 Modern Research Center for Traditional Chinese Medicine; Shanxi Key Laboratory of Active Constituents Research and Utilization of TCM, Shanxi University, Taiyuan 030006, China
2 Departments of Chemistry, University of Louisville, Louisville, KY 40292, USA

Date of Web Publication6-Nov-2018

Correspondence Address:
Xue-Mei Qin
Modern Research Center for Traditional Chinese Medicine, Shanxi University, Xiaodian, Taiyuan 030006
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/wjtcm.wjtcm_20_18

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  Abstract 


Objective: Depression is one of the prevalent and prominent complex psychiatric diseases, and the number of depressed patients has been on the rise globally during the recent decades. Xiaoyaosan, as a famous Chinese herbal formula, has been widely used in depression patients for a long time. However, the therapeutic mechanisms remain uncertain because of the difficulty of depression pathophysiology and the lack of bioinformatic approach to understand the molecular connection. Materials and Methods: In this thesis, we applied a network pharmacology approach to explain the potential mechanisms between Xiaoyaosan and depression involved in oral bioavailability screening, drug-likeness assessment, caco-2 permeability, blood–brain barrier target recognition, and network analysis. Results: Sixty-six active compounds in Xiaoyaosan formula with favorable pharmacokinetic profiles are predicted as active compounds for antidepression treatment. Network analyses showed that these 66 compounds target 40 depression-associated proteins including especially HTR2A, NR 3C1, monoamine oxidase inhibitor B, XDH, and CNR2. These proteins are mainly involved in the neuroactive ligand–receptor interaction, serotonergic synapse, cAMP signaling pathways, and calcium signaling pathways. Conclusion: The integrated network pharmacology method can provide a new approach for understanding the pathogenesis of depression and uncovering the molecular mechanisms of Xiaoyansan, which will also facilitate the application of traditional Chinese herbs in modern medicine.

Keywords: Depression, mechanism, network pharmacology, Xiaoyaosan formula


How to cite this article:
Gao Y, Gao L, Tian JS, Qin XM, Zhang X. A network pharmacology approach to decipher the mechanisms of anti-depression of Xiaoyaosan formula. World J Tradit Chin Med 2018;4:147-62

How to cite this URL:
Gao Y, Gao L, Tian JS, Qin XM, Zhang X. A network pharmacology approach to decipher the mechanisms of anti-depression of Xiaoyaosan formula. World J Tradit Chin Med [serial online] 2018 [cited 2018 Nov 20];4:147-62. Available from: http://www.wjtcm.net/text.asp?2018/4/4/147/245062




  Introduction Top


Depression is a prevalent and prominent complex psychiatric disease, which affects 350 million people and their families in the world.[1] Furthermore, depression is predicted to become the second biggest contributor to the global burden of disease and disability by the year 2020 according to the WHO's prediction.[2] In recent years, common antidepressants involve selective serotonin reuptake inhibitors and serotonin and norepinephrine (NE) reuptake inhibitors, and so on. Nevertheless, their therapeutic results are hindered by various side effects such as psychomotor impairment and dependence liability.[3],[4] Therefore, studies looking for more effective antidepressant therapies and/or therapies with fewer or no adverse effects are severely needed.

Nowadays, modernization of traditional Chinese medicine (TCM) has caught global attention, thanks to its particular theory and clinical application. And, rather than conventional medicines, TCM acts as an important part in the health insurance for people due to its moderate treatment effects and lower side effects.[5] Therefore, a perfect antidepressant from TCM is urgently required.

Xiaoyaosan, one of the famous traditional Chinese formulas for the treatment of depression, was from the book of Taiping Huimin Heji Jufang in the Song Dynasty (960–1127 A.D.). This formula was made up of eight commonly used drugs including Radix Bupleuri (Bupleurum chinense DC.), Radix Angelicae Sinensis (Angelica sinensis [Oliv.] Diel), Radix Paeoniae Alba (Paeonia lactiflora Pall.), Rhizoma Atractylodis Macrocephalae (Atractylodes macrocephala Koidz.), Rhizoma Zingiberis Recens (Zingiber officinale Rosc.), Radix Glycyrrhizae (Glycyrrhiza uralensis Fisch.), Herba Menthae (Mentha haplocalyx Briq.), and Poria (Poria cocos [Schw.] Wolf) with dose ratio of 6:6:6:6:6:3:2:2. Xiaoyaosan formula is officially registered in Chinese Pharmacopoeia and has some positive influences, involving soothing the liver, enhancing the circulation of Qi to alleviate depression, strengthening the spleen, and nourishing blood.[6] Up to now, clinically, a large number of verification has revealed that Xiaoyaosan formula has therapeutic influences on depression.[7] In our previous experiments, biochemical and behavior investigations indicate that Xiaoyaosan formula has noticeable antidepression activity through the 5-hydroxytryptamine (5-HT), hypothalamic–pituitary–adrenal (HPA) axis function, and neuroinflammation.[8] However, the mechanisms of Xiaoyaosan formula on depression are still unknown.

Fortunately, in recent years, as an emerging field, network pharmacology has connected with the pharmacokinetics and pharmacodynamics analysis, combined with the drug–target–disease network is developed to overcome these challenges in the herbal formula.[9] Network pharmacology-based study of TCM may provide holistic approaches for assessing the drug action and comprehending the therapeutic mechanisms in the context of a molecular network. Recently, network pharmacology approach has shown the value of application in the elucidation of the therapeutic mechanisms of a series of TCMs, such as Liu-Wei-Di-Huang pill,[10] Ma huang Decoction,[11] and Danshen formula.[12]

In this thesis, the author used a network pharmacology approach as an instrument to decipher the mechanisms of antidepressive action of Xiaoyaosan formula. This approach involves incorporating the drug absorption, distribution, metabolism, and excretion (ADME) screening, target predictors, and network construction techniques. Particularly, we firstly applied four pharmacokinetic models, involving drug-likeness (DL), oral bioavailability (OB), caco-2 permeability, and blood–brain barrier (BBB), to screen the active constituents with promising ADME profiles from Xiaoyaosan formula. The objective is to generate an opportunity to increase the recent understanding of the efficiency of Xiaoyaosan formula for the treatment of depression.


  Materials and Methods Top


Protocol

In order to disclose the antidepressive mechanisms of Xiaoyaosan formula, we have employed an integrated network pharmacology approach including the ADME screening, drug target, and network analysis, which is introduced to explore the nature of holistic Xiaoyaosan formula. The tactic is as follows [Figure 1].
Figure 1: Framework of network pharmacology used to study the mechanism of Xiaoyaosan in the treatment of depression. The protocol includes incorporating the drug absorption, distribution, metabolism, and excretion screening, target predictors, and network construction techniques

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Xiaoyaosan formula database constructions

In order to obtain the known chemical compounds in Xiaoyaosan formula, a large number of compounds were manually obtained from the Chinese Medicine Systems Pharmacology Database and Analysis Platform (Traditional Chinese Medicine Systems Pharmacology (TCMSP, http://ibts.hkbu.edu.hk/LSP/tcmsp.php),[13] which possesses 31,871 organic molecules identified from >500 effective herbs in TCM. As a chemical-oriented herbal encyclopedia, it is proficient to provide detailed, accurate, and up-to-date quantitative analysis or molecular-scale information about herbal structural data, along with the biological or physicochemical results of drug actions containing molecular weight, OB, DL, intestinal epithelial permeability and aqueous solubility, drug targets, and their relationships with diseases. In total, compounds of Xiaoyaosan formula were extracted and then added into the ingredient database. All structures of these compounds were saved as mol2 format, and subsequently optimized by Sybyl 6.9 (Tripos Associates, St. Louis, MO, USA) with the same parameters.

Oral bioavailability evaluations

OB in vivo, the portion of oral dose of compounds that delivers the systemic circulation in the TCM treatment is undoubtedly one of the vital pharmacokinetic parameters in drug-filtering cascades.[14] In this work, the OB was calculated by using an in-house system OBioavail 1.1 that integrated the metabolism (cytochrome P450s) and transport information (P-glycoprotein [P-gp]), which has been successfully applied in many drug-screening studies.[15] This model was constructed by a dataset of 805 structurally varied drugs and drug-like molecules.[15] Multiple linear regression, partial least square, and support vector machine methods were adopted during this model building, finishing up with determination coefficient (r2) of 0.80 and standard error of estimate = 0.31 for test sets.

Lastly, due to having suitable OB threshold ≥30%, herbal compounds were selected as the candidate compounds for subsequent research analysis. Normally, the first-pass extraction ratio of the gut and liver was 43% and 44%, respectively.[11] Besides, considering the fact that those compounds in medicinal herbs with glycosides groups are usually metabolites to liberate aglycone by the rule of glycosidase hydrolysis reaction before being absorbed, a number of aglycones in the herbs are also taken into account and included in the compound databases. The threshold operated here is largely for (1) obtaining information from the Xiaoyaosan formula as much as possible with the minimum number of components[9] and (2) reasonably clarifying the acquiring model by the reported pharmacological data.[16]

Drug-likeness evaluations

Drug-likeness generally means “molecule which holds functional groups and/or has physical properties consistent with the majority of known drugs”. It helps us to select the compounds in herbs, many of which are chemically and pharmacologically suitable for drugs.[17] In this study, we employed an in silico model to calculate the DL index for each compound. This model was constructed based on all the drugs and DL molecules of DrugBank database (http://www.drugbank.ca/), i.e., 6511 structurally diverse compounds. All descriptors that were calculated by Dragon software version 5.4 (Talete company, Italian) had been introduced to this model. The classifications of these parameters range from constitutional parameters (such as molecular weight), one-dimensional descriptors (such as logP and number of H-donors and H-acceptors), two-dimensional profiles (e.g., polarity number and Global Topological Charge Index), three-dimensional factors (e.g., average geometric distance degree and radius of gyration), and other parameters (such as total positive and negative charges). After removing the descriptors without significant differences, 1533 parameters were finally employed to build this model, based on Tanimoto coefficient (as displayed in equation (1)) which has been successfully applied in many drug-screening studies.[18] For example, the drug similarity (DL) of kaempferol is 0.24.



Where A is the molecular properties of a compound and B is the average molecular property in Drugbank database (http://www.drugbank.ca/) calculated in Dragon software.[19] The higher DL index an herbal ingredient has, the larger opportunity it may enjoy. In this work, according to this principle, a molecule with suitable DL ≥0.18 was regarded as “drug-like” compounds and chosen as the candidate compounds for further research. The threshold of DL is established upon the fact that the average DL index in the Drugbank of these 6511 molecules is 0.18.

Caco-2 cell permeability evaluations

The majority of drug absorption occurs in the small intestine, where the presence of villi and microvilli significantly growths the surface accessible for absorption.[20] Human intestinal cell line caco-2 is generally widely applied to predict the oral absorption properties of drugs across the intestinal epithelial cell barrier, which have been generally found and used in the processes of drug discovery and development.[21] Now, we adopted a robust in silico caco-2 permeability prediction model, which was constructed by 100 drug molecules with satisfactory statistical results (R2 > 0.8) in order to choose compounds that are more possible to own good permeabilities, which has been successfully applied in many drug-screening studies. Finally, the threshold of caco-2 permeability is set to − 0.4, taking the fact that molecule with caco-2 value < −0.4 is not permeable into consideration.

Blood–brain barrier evaluations

BBB is a necessary condition for central nervous system (CNS) to separate the brain from the systemic blood circulation. It normally functions to limit the passage of potentially diagnostic and the therapy agents into the brain parenchyma.[22],[23] Hence, we employed a reliable BBB model described to assess the abilities of compounds which can spread via the BBB to CNS, which has been successfully applied in many drug-screening studies. In this model, the database contained 190 associated but chemically diverse molecules which are either penetrating or nonpenetrating across the BBB.[24] Partial least square discriminant analysis was used to construct the statistical model with two significant potential variables. Due to the fact that compounds with BBB values < −0.3 are considered as nonpenetrating (BBB−), from −0.3 to +0.3 are considered as moderate penetrating (BBB±), and >0.3 are considered as strong penetrating (BBB+).

Predicting and validating the potential targets

Identifying the target proteins of herbal bioactive compounds in Xiaoyaosan formula is a significant step following the discovery of active molecules that obtain biological phenotypes.[25] Therefore, after screening the active compounds of herbs, we applied an innovative algorithm called weighted ensemble similarity to identify the molecular targets, which was developed based on a large-scale data set including 98,327 drug target relationships.[26] The detailed identification procedure has three steps: identifying the structural and physicochemical features of the herbal compounds (cyclin-dependent kinase and Dragon), which are highly correlated to the pharmacological properties in a framework of ensemble; determining a drug's affiliation of a target through assessing the overall similarity of an ensemble; integrating the standardized ensemble similarities (Z score) by Bayesian network to make projections. The optimal model has good specificity and sensitivity (area under the curve = 0.85), as well as internal, external, and experimental test accuracies of 78%, 70%, and 71%, respectively.[26] In this study, all molecular targets achieved above were sent to connect with database UniProt (http://www.uniprot.org/) for target name standardization, which were further subjected to PharmGkb (http://www.pharmgkb.org),[27] therapeutic target database (TTD),[28] and the comparative toxicogenomics database[29] to eliminate the noise, errors, and overlaps to ensure the quality of target database.

Network construction and analysis

To further interpreting the fundamental molecular mechanisms of Xiaoyaosan formula, we established three matching networks. First, active compounds of Xiaoyaosan formula and their relevant targets were applied in generating the compound-target network (C-T network), in which a compound and a target are related with each other if this protein is a known or validated target of this compound. Second, a target-disease network (T-D network) was found after connecting the predicted targets and associated illnesses. We firstly excavated the associated disease of all targets from the databases of TTD and PharmGKB, and all targets and their corresponding disorders were used to build a bipartite graph of T-D network. Third, we fetched the exact pathway information of targets from the database of Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/), and then built a Target-Pathway (T-P network) bipartite graph which showed targets and their corresponding canonical pathways to elucidate the mechanisms between Xiaoyaosan and depression. All imagined network graphs were created by Cytoscape 3.2.1,[30] an open software package project for visualizing, integrating, and analyzing the molecular and genetic communication networks.


  Results and Discussion Top


TCM formula, characteristic of using multiple herbs together, might instantaneously target various physiological processes to arouse the whole body's potential capability to recover to health.[31] Plentiful experiments and sufficient clinical data uncover that, compared with the conventional Food and Drug Administration-approved agent, TCM formula holds various active compounds, not merely reinforces the therapeutic efficacy of single drugs, but weakens the side effect of its main compounds through in vivo drug and drug interactions. Nevertheless, in spite of the distinguished therapy of such herbs for the remedy of depression, their molecular mechanism for the Xiaoyaosan formula functions remains uncertain owing to the same features of multicomponents, multitargets, and multipharmacologic effects. Fortunately, current developments in systems biology and medicine have urged the application of network pharmacology method in the research of the botanic drugs.

Hence, the present study employs bioinformatics methods and approaches to demonstrate the active compounds, target identification, and network analysis of Xiaoyaosan formula to accurately decipher its therapeutic mechanism of action of herbal medicines in depression management.

Active compound identification

A number of 1357 compounds in Xiaoyaosan formula are finally screened from extensive literatures and TCMSP database, of which 357 are in Radix Bupleuri, 125 in Radix Angelicae Sinensis, 85 in Radix Paeoniae Alba, 55 in Rhizoma Atractylodis Macrocephalae, 280 in Radix Glycyrrhizae, 34 in Poria, 164 in Herba Menthae, and 265 in Rhizoma Zingiberis Recens.

Absorption, distribution, metabolism, and excretion prediction of Xiaoyaosan formula

Generally, pharmacokinetic substances significantly affect the effects of drugs. Therefore, it is necessary to filter active compounds with promising pharmacokinetic properties in order to recognize the therapeutic effects of TCM. In the present study, we firstly used four typical ADME parameters, including the evaluation of OB, DL, caco-2 permeability, and BBB to recognize the promising compounds of Xiaoyaosan formula. In this part, OB and DL filtrating are deemed as positive factors to distinguish pharmaceutically favorable compounds in Xiaoyaosan formula, on the basis of the specified principles: OB >30% and DL >0.18 and compounds described as favorable compounds, even though these compounds do not obey the above principles. Furthermore, a total of 66 active chemicals of the 1357 compounds were selected for favorable pharmacokinetic profiles [Table 1].
Table 1: 66 compounds from Xiaoyaosan formula and corresponding predicted oral bioavailability, drug-likeness, Caco-2 cell permeability, blood-brain barrier

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In Xiaoyaosan formula, 14 compounds of Radix Bupleuri were screened and reserved for additional research. Among these compounds, saikosaponin a (M2, OB = 32.39%, DL = 0.63), saikosaponin c (M1, OB = 54.22%, DL = 0.63), saikosaponin d (M3, OB = 34.39%, DL = 0.63), quercetin (M4, OB = 42.41%, DL = 0.28), rutin (M6, OB = 47.46%, DL = 0.28), hyperoside (M7, OB = 35.50%, DL = 0.77), and kaempferol (M66, OB = 42.30%, DL = 0.24) have previously been reported as active compounds. For example, saikosaponin a is a major triterpenoid saponin isolated from Radix Bupleuri, which has a potential increase in 5-HT.[32] Saikosaponin d antagonizes corticosterone-induced apoptosis through regulation of mitochondrial glutamate receptor (GR) translocation and a GR-dependent pathway.[33]

Interestingly, we displayed that Radix Bupleuri shares the same compounds with other Chinese herbs. For instance, kaempferol overlaps with Radix Paeoniae Alba and Radix Glycyrrhizae. Alloaromadendrene (M62, OB = 53.54%, DL = 0.10) and vanillin (M64, OB = 69.24%, DL = 0.03) overlap with Radix Bupleuri and Rhizoma Zingiberis Recens and isoliquiritigenin (M63, OB = 89.47%, DL = 0.18) overlaps with Radix Bupleuri and Radix Paeoniae Alba, both of which play an important role in depression diseases. For instance, studies have also suggested that as a monoamine oxidase inhibitor (MAOI) with potential neuroprotection, kaempferol has been proved to be potent MAOIs.[34] In addition, quercetin attenuates the hyperactivity of the HPA axis activation through the suppression of the corticotropin-releasing factor mRNA expression,[35] and data suggest antidepressant capabilities for rutin and quercetin with inhibition of MAO at least as part of the mechanism of action.[36] Therefore, these 14 compounds were finally obtained for profound analysis.

In the treatment of depression, Radix Angelicae Sinensis could not only enrich the blood to nourish the liver body and stimulate the circulation of blood to open the heart arteries and veins, but also relax the bowels. The compound prescription of antidepressant, in which the Radix Angelicae Sinensis plays an important role, is widely used in clinics, such as ferulic acid (M11, OB = 55.14%, DL = 0.06), ligustilide (M2, OB = 51.30%, DL = 0.07), butylidenephthalide (M13, OB = 51.30%, DL = 0.07), and β-sitosterol (M65, OB = 45.04%, DL = 0.75). Ferulic acid and ligustilide are major bioactive compounds isolated from Radix Angelicae Sinensis, which could be effortlessly absorbed and metabolized in the human body, which has abundant bioactive activities, including antioxidant and anti-inflammatory activities. Thus, ferulic acid plays a vital role in the action of neurodegenerative disorders, scavenging free radicals, and controlling the expression and/or activity of cytotoxic enzymes.[37] In addition, ligustilide acts as a promising nephron-protective drug by the control of oxidative stress.[38]

The predicted active compounds in Radix Paeoniae Alba, which have favorable ADME features, are paeoniflorin (M14, OB = 32.58%, DL = 0.79), albiflorin (M15, OB = 60.64%, DL = 0.77), benzoic acid (M16, OB = 30.15%, DL = 0.03), and cianidanol (M17, OB = 59.25%, DL = 0.24). Remarkably, compounds such as paeoniflorin and albiflorin have been convincingly revealed to have worth mentioning antidepressant activity. In addition, paeoniflorin and its enriched glycosides have been verified to deliver an antidepressant-like effect in animal models of behavioral despair, while albiflorin plays a role in depression and seizure.[39] Paeoniflorin was appeared to increase cell viability and reduce the level of intracellular reactive oxygen species and malondialdehyde in corticosterone-treated PC12 cells.[40] Paeoniflorin also reversed the nerve growth factor mRNA level affected by corticosterone in PC12 cells.[39] In addition, benzoic acid and cianidanol also have been performing as having good OBs and DLs.

Target proteins of Xiaoyaosan formula

In general, TCM formula contains abundant pharmacological compounds, which provide clever views for the prevention and treatment of complicated ascites diseases in a synergistic approach. To comprehend the core mechanism of such synergistic effect, it is significant to disclose the potential targets of drugs. Nevertheless, experimental approaches for exploring targets of drugs are challenging and time consuming. By employing SysDT technique, a number of 189 pharmacological targets have been recognized with the biological compounds, in which two or more potential compounds target several proteins concurrently. For instance, molecule quercetin can attenuate the function of MAOB, a particular target for the action of depression. Besides, isoliquiritigenin and kaempferol both show interactions on MAOB in our prediction, indicating potential synergistic mechanism in Xiaoyaosan formula combination for treating the depression.

Compound-target network of Xiaoyaosan formula

The C-T network is identified as a mathematics bipartite communication network where the color-coded nodes represent herbal drugs and their target proteins, and an edge links a drug node to a protein node if the protein is the target of the drug [Figure 2] and [Table S1]. To comprehend the association between the constituents of the Chinese herbs and relevant targets, first of all, the C-T network was created after attaching 66 compounds to 189 protein targets. The network contained 255 nodes and 971 edges, bringing about an average degree of 4.4 nodes per target and 15.16 edges per compound, respectively.
Figure 2: Compound-target network of Xiaoyaosan formula. A compound node (M-number) and a target node (gene) are linked if the protein is targeted by the corresponding compound. M1-M10 from RB; M11-M13 from RAS; M14-M17 from RPA; M18-M21 from RAM; M22-M28 from P; M29-M45 from RG; M46-M52 from RZR; M53-M61 from MH; M62 and M63 from RB and RG; M64 from RB and RZR; M65 from RAS and RPA and RG; M66 from RB and RPA and RG

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In the C-T network, it is obvious that most of the drugs show correlative polypharmacology; in other words, a drug attaches to beyond one target. Targets of the central circle displayed more connections with potential compounds than those in the external circle, indicating that lots of potential targets are not recognized found on a single compound but via various ligands, whereas some targets are controlled via a single compound. Therefore, resulting in the therapeutic polypharmacology, the potential polypharmacological effects of all active compounds in this work are involved in the modulation of multiple targets, such as, the highly associated nodes of quercetin (M4, degree = 88, betweenness = 0.309827) and rutin (M34, degree = 41, betweenness = 0.267833). For example, quercetin is one of the significant hubs corresponding to various target clusters. It has high attractions with 5-HT receptor 2C (HTR2C), amine oxidase (flavin-containing) B (MAOB), 5-HT receptor 5A (HTR5A), and so on, which bring a highly complicated pharmacological outline as well as antidepression and antioxidant properties and so forth. It is likewise obvious that most of the target proteins are cross-linked together in this network. Therefore, improving pharmacological synergies could be occurred between effective compounds owing to the point that different drugs have effects on the same receptor target. For instance, 5-HT receptor 2A (HTR2A, degree = 25, betweenness = 0.397,091), a famous proteolytic enzyme, participated in several pathological processes including inflammation and tumor invasion and enjoys the increasing number of associations with glucocorticoid receptor products such as saikosaponin c, paeoniflorin, and Kaempferol. To sum up, the C-T network analysis could present understandings into the drug and target interaction, such as target binding, therapeutic pharmacology, and herbal synergy.

Target-disease network of Xiaoyaosan formula

Depression is often regarded as a feeling of increasing our vulnerability to other complex diseases, for example, Type II diabetes[41] and cardiovascular disease.[42] A noteworthy target intersection is discovered among the herbs of Xiaoyaosan formula. We retrieved forty potential targets [Table 2] and [Figure 3] out of the potential targets accompanying with depression-connected diseases, and according to the US National Library's Medical Subject Headings, these diseases were organized into 26 clusters, such as nervous system diseases and mental disorders. Consequently, we created a T-D network given rise to 351 T-D associates involving 40 targets of 26 diseases [Figure 4], [Figure 5] and [Table S2], and approximately half of the targets relate with various diseases. As shown in [Figure 4], Xiaoyaosan treats depression through the corresponding targets and can also treat other types of diseases through these same targets; for example, nervous system diseases, neoplasms, pathological conditions' signs and symptoms, and cardiovascular diseases. This shows that depression and other diseases may pass the same pathological mechanism.
Table 2: The depression-related and their network parameters

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Figure 3: Compound-target (depression) network of Xiaoyaosan formula. A compound node (M-number) and a target node (gene) are linked if the protein is targeted by the corresponding compound. M1-M10 from RB; M11-M13 from RAS; M14-M17 from RPA; M18-M21 from RAM; M22-M28 from P; M29-M45 from RG; M46-M52 from RZR; M53-M61 from MH; M62 and M63 from RB and RG; M64 from RB and RZR; M65 from RAS and RPA and RG; M66 from RB and RPA and RG

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Figure 4: Target-disease network of Xiaoyaosan formula. A target node (circle, light blue) and a disease node (square, pink) are connected if the disease is targeted by the corresponding protein

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Figure 5: Compounds-targets-diseases network of Xiaoyaosan formula where pink nodes represent the compounds, blue nodes represent the targets, and green nodes represent the diseases. The compound-target-pathway network is built by a compound, a target, and a disease if they have a corresponding relationship. The information of diseases is obtained by mapping the target proteins to the US National Library's Medical Subject Headings

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Network-connecting potential targets with diseases are constructed for exploring the protein interactions and the therapeutic targets for diseases. It has been demonstrated that different diseases might share common symptoms, thus potentially be cured by the same formula. In other words, Xiaoyaosan formula may possibly be used to treat various diseases. The coinciding targets among these diseases show that various diseases share the same pathological changes and could be treated by a common herbal mixture. For instance, HTR2A is a neurotransmitter that occupies a significant character in neurobiology. Irregularity of the serotonergic system has been involved in a number of human diseases, such as mental depression, migraine, epilepsy, obsessive-compulsive disorder, and affective disorder. It is a recent target for pharmaceutical mediation against depression diseases and has great affinities with compounds such as hyperoside and licochalcone A. Xiaoyaosan formula might share multiple targets to cure different diseases.

Target-pathway network of Xiaoyaosan formula

In order to precisely explain the fundamental therapeutic mechanisms of Xiaoyaosan formula, we obtained the acknowledged pathways that may be correlated with the depression prevention and treatment from KEGG database, which turns out to be 39 KEGG pathways [Figure 6] and [Table S3], [Table S4], such as neuroactive ligand–receptor interaction (hsa04080), serotonergic synapse (hsa04726), cAMP signaling pathway (hsa04024), and calcium signaling pathway (hsa04020).
Figure 6: Target-pathway network of Xiaoyaosan formula where green nodes represent the targets and purple nodes represent the pathways. The target-pathway network is built by a target and a pathway if the pathway is lighted at the target. The information of pathways is obtained by mapping the target proteins to the Kyoto Encyclopedia of Genes and Genomes pathway database

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Through studies into the C-T, T-D, and T-P network, a visible and systematic depiction of interactions between Xiaoyaosan, targets of depression, was acquired, even though the detailed and accurate mechanisms of compounds in Xiaoyaosan formula for depression remain uncertain. To better distinguish the complete regulation of Xiaoyaosan formula, we created the merged depression pathway. The potential target proteins display noteworthy roles in the depression pathways [Figure 7]. The NE signaling via G-protein receptors gives rise to the motivation of Akt by interruption of PP2A. Phosphorylated Akt can phosphorylate the N-terminal Ser (serine) of GSK3 β, giving rise to the suppression of GSK3 β activity. To our knowledge, GSK3 β is a key downstream target for depression, indicating that the binders, for example, quercetin and rutin, joined to ADRB1, ADRB2, and SLC6A2, revealing expected antidepressant effects.
Figure 7: Depression-relating pathway. The blue nodes are potential protein targets. And, the light blue nodes are relevant targets in the pathway

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Furthermore, serotonergic synapse is a functional treatment choice on patients with depression. Quercetin might inhibit presynaptic receptor SLC6A4, giving rise to a growing 5-HT content in the synaptic cleft, therefore negatively controlling the desensitization of postsynaptic receptors HTR1A, HTR2A, HTR2C, HTR5A, and HTR6 in the pathway. After the communication with 5-HT, the core signaling connection for HTR2A and HTR2C receptors will trigger phospholipase C, beta, via pairing with guanine nucleotide-binding protein, q polypeptide. The core signaling pathway to HTR1A receptors is via a guanine nucleotide-binding protein, alpha inhibiting activity polypeptide, resulting in the reduction of cAMP construction through inhibiting the adenylate cyclase. It finally activates the CREB, a target of antidepressants that associates with mood stabilization located at the downstream of the pathway.[43]

In addition, based on these results, it is rational to put forward that depression possibly targets precise pathogenic targets of the complicated diseases to cure a lot of disorders via the use of Xiaoyaosan formula. Consequently, these results could offer helpful evidence to discover the original therapeutic special effects of depression for related diseases.


  Conclusions Top


In this work, we used a novel system tactic which combines the computational models with network pharmacology methods to dissect the underlying mechanisms of action of Xiaoyaosan and detect the pathogenesis of depression. The main findings are as follows:

  1. Sixty-six bioactive compounds and forty target proteins were obtained in this study, demonstrating a multidrug–multitarget paradigm of Xiaoyaosan. These compounds and targets might serve to guide our further study of this botanical drug
  2. C-T and T-D network analyses together display that some vital compounds of Xiaoyaosan such as quercetin, rutin and kaempferol may play an important role in the treatment of depression, and Xiaoyaosan positively aiming for some targets such as HTR2A, NR3C1, MAOB, XDH, and CNR2 exhibits the therapeutic effects against depression, accompanying symptoms such as anxiety disorder, Parkinson's disease, and nervous system diseases
  3. The Target-pathway network integrated depression pathways display that Xiaoyaosan might together act various pathways involved in the pathogenesis of depression, which further demonstrate the main pathways of depression: neuroactive ligand–receptor interaction, serotonergic synapse, cAMP signaling pathways, and calcium signaling pathways
  4. The work provides a new approach for understanding the mechanisms of pathogenesis of depression and the action mechanism of Xiaoyaosan on depression from molecular to pathway level. The results will facilitate the widespread application of traditional Chinese herbs in modern medicine.


Acknowledgments

This scientific work was supported by The key R and D projects of Shanxi Province (No. 201603D3113013); The Science and Technology of Shanxi Province (No. 201603D321077 and 201701D121137); The Creative Project for Postgraduate Education in Shanxi province in 2016 (No. 2016SY007); Shanxi Key Laboratory of Active constituents Research and Utilization of TCM (No. 201605D111004); and the Program for the Outstanding Innovative Teams of Higher Learning Institutions of Shanxi (OIT) (No. 201605D131045-18). The authors also give gratitude to Prof. Yong-Hua Wang and the assistance of the systems pharmacology research group.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Rucker JJ, Breen G, Pinto D, Pedroso I, Lewis CM, Cohen-Woods S, et al. Genome-wide association analysis of copy number variation in recurrent depressive disorder. Mol Psychiatry 2013;18:183-9.  Back to cited text no. 1
    
2.
Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE, et al. Global burden of disease attributable to mental and substance use disorders: Findings from the Global Burden of Disease Study 2010. Lancet 2013;382:1575-86.  Back to cited text no. 2
    
3.
Cao X, Li LP, Wang Q, Wu Q, Hu HH, Zhang M, et al. Astrocyte-derived ATP modulates depressive-like behaviors. Nat Med 2013;19:773-7.  Back to cited text no. 3
    
4.
Mao YM, Zhang MD. Augmentation with antidepressants in schizophrenia treatment: Benefit or risk. Neuropsychiatr Dis Treat 2015;11:701-13.  Back to cited text no. 4
    
5.
Zhang B, Fu Y, Huang C, Zheng C, Wu Z, Zhang W, et al. New strategy for drug discovery by large-scale association analysis of molecular networks of different species. Sci Rep 2016;6:21872.  Back to cited text no. 5
    
6.
Biémont C, Vieira C. Could interallelic interactions be a key to the epigenetic aspects of fitness-trait inbreeding depression? Heredity (Edinb) 2014;112:219-20.  Back to cited text no. 6
    
7.
Tian JS, Peng GJ, Gao XX, Zhou YZ, Xing J, Qin XM, et al. Dynamic analysis of the endogenous metabolites in depressed patients treated with TCM formula Xiaoyaosan using urinary (1)H NMR-based metabolomics. J Ethnopharmacol 2014;158(Pt A):1-10.  Back to cited text no. 7
    
8.
Gao X, Zheng X, Li Z, Zhou Y, Sun H, Zhang L, et al. Metabonomic study on chronic unpredictable mild stress and intervention effects of Xiaoyaosan in rats using gas chromatography coupled with mass spectrometry. J Ethnopharmacol 2011;137:690-9.  Back to cited text no. 8
    
9.
Li X, Xu X, Wang J, Yu H, Wang X, Yang H, et al. A system-level investigation into the mechanisms of Chinese traditional medicine: Compound Danshen formula for cardiovascular disease treatment. PLoS One 2012;7:e43918.  Back to cited text no. 9
    
10.
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;10:1014-22.  Back to cited text no. 10
    
11.
Yao Y, Zhang X, Wang Z, Zheng C, Li P, Huang C, et al. Deciphering the combination principles of traditional Chinese medicine from a systems pharmacology perspective based on Ma-Huang decoction. J Ethnopharmacol 2013;150:619-38.  Back to cited text no. 11
    
12.
Zhou W, Wang J, Wu Z, Huang C, Lu A, Wang Y, et al. Systems pharmacology exploration of botanic drug pairs reveals the mechanism for treating different diseases. Sci Rep 2016;6:36985.  Back to cited text no. 12
    
13.
Ru J, Li P, Wang J, Zhou W, Li B, Huang C, et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J Cheminform 2014;6:13.  Back to cited text no. 13
    
14.
Varma MV, Obach RS, Rotter C, Miller HR, Chang G, Steyn SJ, et al. Physicochemical space for optimum oral bioavailability: Contribution of human intestinal absorption and first-pass elimination. J Med Chem 2010;53:1098-108.  Back to cited text no. 14
    
15.
Xu X, Zhang W, Huang C, Li Y, Yu H, Wang Y, et al. A novel chemometric method for the prediction of human oral bioavailability. Int J Mol Sci 2012;13:6964-82.  Back to cited text no. 15
    
16.
Wang X, Xu X, Li Y, Li X, Tao W, Li B, et al. Systems pharmacology uncovers Janus functions of botanical drugs: Activation of host defense system and inhibition of influenza virus replication. Integr Biol (Camb) 2013;5:351-71.  Back to cited text no. 16
    
17.
Ma C, Wang L, Xie XQ. GPU accelerated chemical similarity calculation for compound library comparison. J Chem Inf Model 2011;51:1521-7.  Back to cited text no. 17
    
18.
Tao W, Xu X, Wang X, Li B, Wang Y, Li Y, et al. Network pharmacology-based prediction of the active ingredients and potential targets of Chinese herbal radix curcumae formula for application to cardiovascular disease. J Ethnopharmacol 2013;145:1-10.  Back to cited text no. 18
    
19.
Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. DrugBank: A comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 2006;34:D668-72.  Back to cited text no. 19
    
20.
Pang KS. Modeling of intestinal drug absorption: Roles of transporters and metabolic enzymes (for the Gillette review series). Drug Metab Dispos 2003;31:1507-19.  Back to cited text no. 20
    
21.
Pham The H, González-Álvarez I, Bermejo M, Mangas Sanjuan V, Centelles I, Garrigues TM, et al. In silico prediction of caco-2 cell permeability by a classification QSAR approach. Mol Inform 2011;30:376-85.  Back to cited text no. 21
    
22.
Kam A, Li KM, Razmovski-Naumovski V, Nammi S, Chan K, Li Y, et al. The protective effects of natural products on blood–brain barrier breakdown. Curr Med Chem 2012;19:1830-45.  Back to cited text no. 22
    
23.
Tattersall MH, Sodergren JE, Dengupta SK, Trites DH, Modest EJ, Frei E 3rd, et al. Pharmacokinetics of actinoymcin D in patients with malignant melanoma. Clin Pharmacol Ther 1975;17:701-8.  Back to cited text no. 23
    
24.
Liu H, Wang J, Zhou W, Wang Y, Yang L. Systems approaches and polypharmacology for drug discovery from herbal medicines: An example using licorice. J Ethnopharmacol 2013;146:773-93.  Back to cited text no. 24
    
25.
Nidhi, Glick M, Davies JW, Jenkins JL. Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases. J Chem Inf Model 2006;46:1124-33.  Back to cited text no. 25
    
26.
Zheng C, Guo Z, Huang C, Wu Z, Li Y, Chen X, et al. Large-scale direct targeting for drug repositioning and discovery. Sci Rep 2015;5:11970.  Back to cited text no. 26
    
27.
Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K, Thorn CF, et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther 2012;92:414-7.  Back to cited text no. 27
    
28.
Zhu F, Shi Z, Qin C, Tao L, Liu X, Xu F, et al. Therapeutic target database update 2012: A resource for facilitating target-oriented drug discovery. Nucleic Acids Res 2012;40:D1128-36.  Back to cited text no. 28
    
29.
Davis AP, Murphy CG, Johnson R, Lay JM, Lennon-Hopkins K, Saraceni-Richards C, et al. The comparative Toxicogenomics database: Update 2013. Nucleic Acids Res 2013;41:D1104-14.  Back to cited text no. 29
    
30.
Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: New features for data integration and network visualization. Bioinformatics 2011;27:431-2.  Back to cited text no. 30
    
31.
Tian P. Convergence: Where west meets east. Nature 2011;480:S84-6.  Back to cited text no. 31
    
32.
Li LF, Lu J, Li XM, Xu CL, Yang J, Qu R, et al. Antidepressant-like effects of the saponins extracted from Chaihu-Jia-Longgu-Muli-tang in a rat unpredictable chronic mild stress model. Fitoterapia 2012;83:93-103.  Back to cited text no. 32
    
33.
Li ZY, Jiang YM, Liu YM, Guo Z, Shen SN, Liu XM, et al. Saikosaponin D acts against corticosterone-induced apoptosis via regulation of mitochondrial GR translocation and a GR-dependent pathway. Prog Neuropsychopharmacol Biol Psychiatry 2014;53:80-9.  Back to cited text no. 33
    
34.
Sloley BD, Urichuk LJ, Morley P, Durkin J, Shan JJ, Pang PK, et al. Identification of kaempferol as a monoamine oxidase inhibitor and potential neuroprotectant in extracts of Ginkgo biloba leaves. J Pharm Pharmacol 2000;52:451-9.  Back to cited text no. 34
    
35.
Bhutada P, Mundhada Y, Bansod K, Ubgade A, Quazi M, Umathe S, et al. Reversal by quercetin of corticotrophin releasing factor induced anxiety- and depression-like effect in mice. Prog Neuropsychopharmacol Biol Psychiatry 2010;34:955-60.  Back to cited text no. 35
    
36.
Dimpfel W. Rat electropharmacograms of the flavonoids rutin and quercetin in comparison to those of moclobemide and clinically used reference drugs suggest antidepressive and/or neuroprotective action. Phytomedicine 2009;16:287-94.  Back to cited text no. 36
    
37.
Sultana R. Ferulic acid ethyl ester as a potential therapy in neurodegenerative disorders. Biochim Biophys Acta 2012;1822:748-52.  Back to cited text no. 37
    
38.
Luo Y, Wang Q, Zhang Y. A systems pharmacology approach to decipher the mechanism of danggui-shaoyao-san decoction for the treatment of neurodegenerative diseases. J Ethnopharmacol 2016;178:66-81.  Back to cited text no. 38
    
39.
Mao QQ, Zhong XM, Qiu FM, Li ZY, Huang Z. Protective effects of paeoniflorin against corticosterone-induced neurotoxicity in PC12 cells. Phytother Res 2012;26:969-73.  Back to cited text no. 39
    
40.
Mao QQ, Zhong XM, Feng CR, Pan AJ, Li ZY, Huang Z, et al. Protective effects of paeoniflorin against glutamate-induced neurotoxicity in PC12 cells via antioxidant mechanisms and Ca(2+) antagonism. Cell Mol Neurobiol 2010;30:1059-66.  Back to cited text no. 40
    
41.
De la Cruz-Cano E, Tovilla-Zarate CA, Reyes-Ramos E, Gonzalez-Castro TB, Juarez-Castro I, López-Narváez ML, et al. Association between obesity and depression in patients with diabetes mellitus type 2; a study protocol. F1000Res 2015;4:7.  Back to cited text no. 41
    
42.
Seligman F, Nemeroff CB. The interface of depression and cardiovascular disease: Therapeutic implications. Ann N Y Acad Sci 2015;1345:25-35.  Back to cited text no. 42
    
43.
Huang C, Zheng C, Li Y, Wang Y, Lu A, Yang L, et al. Systems pharmacology in drug discovery and therapeutic insight for herbal medicines. Brief Bioinform 2014;15:710-33.  Back to cited text no. 43
    


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