Integrated Analysis of the Mechanisms of Da-Chai-Hu Decoction in Type 2 Diabetes Mellitus by a Network Pharmacology Approach

Background The incidence of type 2 diabetes mellitus (T2DM) has increased year by year, which not only seriously affects people's quality of life, but also imposes a heavy economic burden on the family, society, and country. Currently, the pathogenesis, diagnosis, and treatment of T2DM are still unclear. Therefore, exploration of a precise multitarget treatment strategy is urgent. Here, we attempt to screen out the active components, effective targets, and functional pathways of therapeutic drugs through network pharmacology with taking advantages of traditional Chinese medicine (TCM) formulas for multitarget holistic treatment of diseases to clarify the potential therapeutic mechanism of TCM formulas and provide a systematic and clear thought for T2DM treatment. Methods First, we screened the active components of Da-Chai-Hu Decoction (DCHD) by absorption, distribution, metabolism, excretion, and toxicity (ADME/T) calculation. Second, we predicted and screened the active components of DCHD and its therapeutic targets for T2DM relying on the Traditional Chinese Medicine Systems Pharmacology Analysis Platform (TCMSP database) and Text Mining Tool (GoPubMed database), while using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to obtain T2DM targets. Third, we constructed a network of the active component-target, target-pathway of DCHD using Cytoscape software (http://cytoscape.org/,ver.3.5.1) and then analyzed gene function, related biological processes, and signal pathways through the DAVID database. Results We screened 77 active components from 1278 DCHD components and 116 effective targets from 253 ones. After matching the targets of T2DM, we obtained 38 important targets and 7 core targets were selected through further analysis. Through enrichment analysis, we found that these important targets were mainly involved in many biological processes such as oxidative stress, inflammatory reaction, and apoptosis. After analyzing the relevant pathways, the synthetic pathway for the treatment of T2DM was obtained, which provided a diagnosis-treatment idea for DCHD in the treatment of T2DM. Conclusions This article reveals the mechanism of DCHD in the treatment of T2DM related to inflammatory response and apoptosis through network pharmacology, which lays a foundation for further elucidation of drugs effective targets.


Introduction
Type 2 diabetes mellitus (T2DM), as the most common type of diabetes, refers to metabolic imbalance of glucose, protein, lipid, water, and electrolyte, caused by genetic, environmental, and psychological factors. It is pathophysiologically characterized by a declined ability of insulin to regulate glucose metabolism accompanied by a decrease or relative decrease in insulin secretion due to defects of islet B-cell function. If one's random blood glucose is ≥11.1 mmol/L or fasting blood glucose is ≥7.0 mmol/L, the patient can be diagnosed as diabetes when accompanying with typical symptoms including polydipsia, polyuria, polyphagia, and unexplained weight loss [1]. Diabetes is one of the three major threats to human health. In the later stage, it will lead to the complications such as cardiovascular and cerebrovascular diseases [2], renal injury [3], retinopathy [4], diabetic foot [5], and neurological disease [6], which are the main reasons for its death rate in diabetic patients. Currently, the incidence of diabetes is rising sharply. e number of diabetics worldwide has increased from 108 million in 1980 to 422 million in 2014, an increase of nearly three times after comparison [7]. As the largest number of people with diabetes worldwide [8], the overall prevalence of diabetes is 9.1% in China, with the highest prevalence of 65-74 years old accounting for 14.1% [9]. e high incidence of diabetes not only seriously affects individual's quality of life, but also imposes a heavy economic burden on the family, society, and country. According to the survey, China's medical expenses for diabetes in 2014 reached 80.33 billion yuan, and the per capita treatment cost was 2,188.73 yuan [10], which showed that a large amount of medical and health resources were consumed due to diabetes in China. e hypoglycemic effect of biomedicine has been proved; nevertheless, long-term or even lifelong medication is needed. For patients with advanced diabetes, who often accompany with diabetic complications, therefore, hypoglycemic agents in combination with drugs for complications are often used [1]. On this account, the cost of medication for diabetic patients is high. Even with the firstline hypoglycemic drug, metformin, there are side effects including lactic acidosis and digestive disorders [11,12]. erefore, it is urgent to seek treatment for diabetes with low cost and few side effects. After thousands of years of development and innovation, traditional Chinese medicine (TCM) has achieved a remarkable effect on treating diabetes based on its wholism and pattern identification and treatment at the advantages of low cost and few side effects [13]. TCM has become one of the major complementary and alternative medicines in East Asia, Europe, North America, and other regions [14].
However, due to the multicomponent and multitarget characteristics of TCM formulas, the action mechanism of most TCM formulas for diseases is still unclear. erefore, we attempt to screen out the active components, effective targets, and functional pathways of drugs through network pharmacology and combined with the advantages of TCM classic formulas for the multitarget holistic treatment, to clarify the mechanism of TCM formulas and provide a systematic and clear idea for the treatment of T2DM.
T2DM belongs to "xi� ao kĕ (wasting-thirst, 消渴)" and "splenic pure heat (pí d� an, 脾瘅)" in TCM field. e cause of this disease is attributed to external contraction and internal damage. Externally, constraint of the six pathogenic factors results in the invasion of toxin, dryness-heat, and wind-heat pathogens into zang-fu organs, dryness generating, and liquid being damaged, which finally leads to xi� ao kĕ. Internally, generation of heat because of emotional disorders and constraint of liver qi, damp-heat generated by intemperate eating, or yin depletion and abnormal exuberance of dryness-heat causes xi� ao kĕ. In the early and middle stages of the onset, sturdy patients present with thirst, bitter taste in the mouth, halitosis, polydipsia, polyphagia, irritability, fullness and distension in the hypochondriac regions, dark urine, dry and hard stool, red tongue with yellow coating, and wiry, excess, and powerful pulse. According to pattern differentiation, these symptoms are diagnosed as liverstomach heat pattern and its typical formula is Da-Chai-Hu Decoction (Dà Chái Hú T� ang, DCHD). e application of DCHD was first recorded in Treatise on Febrile and Miscellaneous Diseases. It has a history of more than 2,000 years. DCHD can harmonize shaoyang and drain heat bind in yangming, whose indication is overlap of diseases of shaoyang and yangming. DCHD is composed of Bupleurum, Scutellaria root, peony root, Pinellia Rhizome, immature bitter orange, rhubarb root and rhizome, fresh ginger, and Chinese date. Among them, Bupleurum resolves constraint and soothes the liver and rectifies qi when combined with peony root, one for dissipating and one for contraction. Bupleurum and Scutellaria root can clear liver heat, and immature bitter orange and rhubarb root and rhizome unblock the bowels and discharge heat in the stomach and intestines. Scutellaria root and rhubarb root and rhizome auxiliary with Pinellia Rhizome and fresh ginger open the middle jiao, with acrid herbs opening and bitter herbs promoting descent. Chinese date harmonizes the actions of all herbs in a formula. All herbs together can rectify qi to resolve constraint, clear heat, and dissipate masses so as to resolve constraint and discharge heat in the liver and stomach, and then xi� ao kĕ itself resolves. e results of modern pharmacological experimental research show that the ingredients in DCHD can lower blood sugar. Bupleurum is the chief medicinal of DCHD. Jin et al showed that Bupleurum has the effect of treating diabetes [15]. Baicalin is the main active component of Scutellaria root. Experiment by Kuo et al. proved that the alcohol extract of DCHD can promote the absorption of glucose to reduce blood sugar, primarily by baicalin activating the IRS-1 and glut4 genes and the signaling cascade of 5′ AMP-activated protein kinase (AMPK), PI3K/Akt, and MAPK/ERK [16]. Naringin, the active component of immature bitter orange, combined with berberine can reduce liver lipid by activating AMPK and inhibiting Notch signaling pathway, thereby enhancing insulin sensitivity and improving resistance and remarkably improving oral glucose tolerance test (OGTT) [17]. Peony root can inhibit liver gluconeogenesis and lower blood sugar [18]. Rheum emodin, a medicinal component of rhubarb root and rhizome, can treat T2DM by inhibiting DPP4 protein [19]. Experimental studies have shown that ginger extract, ginger ketone, does have a therapeutic effect on diabetes. In addition, a randomized, double-blind, and placebo-controlled clinical trial has demonstrated that oral ginger supplementation can improve anthropomorphic data in diabetics by reducing the concentration of NF-κB inflammatory factors [20].
Network pharmacology was first proposed by Hopkins in 2007. It is a discipline that studies the occurrence and development of diseases from the perspective of biological network, recognizes the interaction between drugs and the body, and guides the discovery of new drugs. T2DM is a complex metabolic disease determined by multiple genes and multiple factors, not by a single gene or a single factor. erefore, complex diseases often cannot be effective by intervening in a single target, and the TCM formula is composed of various components, which can simultaneously act on multiple targets through multiple pathways [21]. Typical features of TCM treatment of diseases are "multipathway, multicomponent, multitarget," which is consistent with most key ideas of network pharmacology and network biology and is able to treat complex diseases [22]. Network pharmacology aims to reveal the scientific basis of the traditional attributes of TCM. By establishing the molecular connection between TCM formulas and TCM patterns, it explores the action mechanism and the rules of herbal combination in formulas, thus laying the foundation for the development of new drugs. rough network pharmacology, we can not only explore the complex active molecular components and potential molecular targets in TCM formulas, but also understand the molecular relationships between components and components, and between components and complex disease in formulas, so that diseases can be targetedly treated [23]. Understanding the molecular relationship of TCM mentioned above not only provides sufficient theoretical support for TCM research but also enhances the acceptance of TCM worldwide [14]. erefore, network pharmacology is of great significance to protect and develop TCM and promote the modernization of TCM. In recent years, with the rapid development of network pharmacology, more and more multitarget integrated control methods are used to predict the main active components and potential target groups of TCM formulas to determine their pharmacological mechanisms for a certain type of diseases [24]. For example, Mao et al. used network pharmacology to study the pharmacological mechanism of Xueshuan-Xinmai-Ning Tablet in the treatment of coronary heart disease [25]. Hu and Sun used network pharmacology and network topological algorithms to explore new TCM prescriptions for T2DM [26]. Dia et al. used network pharmacology to reveal the pharmacological and molecular mechanisms of Shen-Qi-Di-Huang Decoction in the treatment of diabetic nephropathy, which laid the foundation for further experimental research and expanded the rational application of Shen-Qi-Di-Huang Decoction in clinical practice [27]. Here, we also use the network pharmacology to reveal the pharmacological mechanism of DCHD in the treatment of T2DM from three aspects of active components, potential targets, and synthetic pathways (as shown in Figure 1).  (JF). In order to screen out potential active compounds, the active molecules with high oral bioavailability (OB) in the organic metabolism from these components, we use the six principles of generic drugs of molecular weight (MW), AlogP, Hdon, Hacc, OB, and DL for screening in this work. From Table 1, the abovementioned six principles were used to screen the ingredients of all the herbs in DCHD, 77 potential components were finally obtained, including 8 in Radix Bupleuri, 25 Figure 2).

Materials and Methods
(1) MW Prediction. MW refers to the mass of a molecule. As one of the parameters of Lipinski's "rule of five," it plays a critical role in pharmacological or biological activity because the size of MW will affect the size of the molecular fragment, then affecting the membrane absorption of drugs. Generally speaking, small molecules are more easily absorbed and distributed, while macromolecules are not only less easily absorbed, but also more easily excreted by bile. e components with MV from 180 to 500 Dalton are perceived as most available in drug therapy (MW ≤ 500).
(2) AlogP Prediction. e AlogP is the logarithm of the ratio of the equilibrium concentration of a compound in a nonaqueous phase to its equilibrium concentration in the aqueous phase in a neutral form. e logarithmic value of the partition coefficient P of the compound in octanol/water system is usually used as a measure of AlogP. Abiding by the Ghose-Crippen method, it is calculated from a regression equation based on the hydrophobicity contribution of 120 atom types, including common bonding of H, C, N, O, and S and the halogens (AlogP ≤5) [29].
(3) Hdon and Hacc Prediction. e hydrogen-bond capacity of a drug solute is an important parameter of permeability [30]. To cross the cell membrane, a drug molecule needs to break the hydrogen bonds formed with its aqueous environment [31]. Consequently, Hdon and Hacc negatively influence absorption, namely, these two parameters could influence the interaction of compounds and targets with the criteria of setting (Hdon ≤5 and Hacc ≤10).
(4) OB Prediction. OB is defined as the ratio of the number of active components absorbed into the circulatory system playing roles at the site of action to the sum of active components. OB is one of the most important indicators for evaluating ADME characteristics via bioinformatics. In this work, the OB screening was calculated by a powerful inhouse system, OBioavail1.1 [32], and the compounds with      OB ≥ 30% were filtered for further analysis. e following two basic sections describe the design principles of the threshold: (1) obtained information from the studied medicines using the compounds as little as possible in number and (2) elucidated the stability within reason by the reported pharmacological data [33].
(5) DL Prediction. DL means that a molecule contains some specific functional group or presents with the same or similar physical characteristics as most drugs. As a qualitative profile, DL is frequently used in drug design to evaluate whether a compound is chemically suitable for the drug, and how DL a molecule is with respect to parameters affecting its pharmacodynamic and pharmacokinetic profiles which will ultimately impact its ADME properties [34].
In this study, we performed a database-dependent model pre-DL (predicts drug-likeness) based on the molecular descriptors and Tanimoto coefficient. e DL of the compounds was calculated by the Tanimoto coefficient defined as follows: (1) In this equation, A represents the descriptor of the new numerator and B indicates all the 6511 molecules selected from the DrugBank database (available online at http://www. drugbank.ca). e average of all descriptors was calculated by Dragon and the compounds with DL ≥ 0.18 were selected [35].

Bioactive Component-Target Prediction for Each Herb in DCHD.
After screening the active components, it was quite critical to find the targets of each component. First, in order to obtain putative targets of potential active components in DCHD, we searched for the corresponding targets based on the systematic drug targeting tool (SysDT) as described in our previous work. Second, we screened out drug targets corresponding to the active components that we screened before. Here, the principle of predicting the corresponding targets of pharmaceutical components was based on two mathematical approaches that effectively integrate large-scale chemical, genomic, and pharmacological information to accurately predict drug-target interactions [36], with a concordance rate of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively [28]. Next, the screened effective targets were combined with similar terms, i.e., the duplicates were removed to obtain the potential effective targets of all herbs in DCHD. Finally, we used the UniProtKB search function in the UniProt database (http:// www.uniprot.org/) to obtain the official symbol for each protein by inputting the protein names with the species limited to "Homo sapiens." UniProt database provides the scientific community with comprehensive, high-quality, and free access to protein sequence and functional information  Evidence-Based Complementary and Alternative Medicine [27]. Eventually, 161 targets from DCHD were obtained without repeated targets of the same component (as shown in Table 2).

T2DM-Specific Protein Collection.
Information regarding T2DM-associated target genes was collected from the Comparative Toxicogenomics Database (CTD; http:// ctdbase.org/), the Online Mendelian Inheritance in Man database (OMIM; https://www.ncbi.nlm.nih.gov/omim), and the Human Gene Database (http://www.genecards. org/). CTD is a robust, publicly available database that aims to advance understanding about how environmental exposures affect human health, which provides curated and inferred chemical-disease associations that are real associations extracted from the literature. All the targets of T2DM were deduced from the CTD database, and 200 targets were filtrated from the inference score of ≥57.56 [37]. OMIM is a comprehensive, authoritative compendium of human genes and genetic phenotypes that is freely available and updated regularly, emphasizing on the relationship between phenotype and genotype. OMIM is able to provide references for further research and tools for genomic analysis of cataloged genes [38]. GeneCards is a searchable, integrative database that provides comprehensive, user-friendly information on all annotated and predicted human genes. It automatically integrates genecentric data from 125 web sources, including genomic, transcriptomic, proteomic, genetic, clinical, and functional information.

Protein-Protein Interaction Data.
All the proteinprotein interaction (PPI) data were derived from STRING (https://string-db.org/, ver. 10.5) [39]. To be specific, the potential targets corresponding to the herbs in DCHD and those related to T2DM were imported into the STRING protein interaction database for PPI analysis with the organism defined as "Homo sapiens" and a confidence score of 0.7. e STRING database, updated on online, provides an open-source database and analysis tools for molecular interactions and aims to collect and integrate the information of all functional interactions between the expressed proteins by consolidating known and forecasted protein-protein association data for a large number of organisms [40].

Network Construction.
e network construction was performed as follows: (1) active compound-active compound target network of DCHD was constructed; (2) herb-compound target-T2DM target network was built by connecting the eight DCHD herbs with compound targets of each herb and T2DM targets; and (3) active compound targets-T2DM targets-other human proteins' PPI network was established.
We created the networks by utilizing the network visualization software Cytoscape (http://cytoscape.org/, ver. 3.5.1) [41]. Cytoscape is a graphic display network software for analysis of complex networks, visualization of biological pathways, and intermolecular interactions. Moreover, it also can integrate the complex networks, gene expression products, and other state data.

Network Topological Features.
ere are three parameters used to evaluate every node in a network, including degree, node betweenness, and closeness. Degree indicates the number of edges between a node and other nodes in a network [42]. Betweenness evaluates the participation of a node in the shortest parts of the network and reflects the ability of nodes to proceed the rate of information flow in the network as well [43]. Closeness refers to the inverse of the sum of the distance from a node to other nodes [44]. e levels of the aforementioned three parameters reflect the importance of a node in the network. e higher the value of the parameter is, the more important the node becomes.

Gene Ontology (GO) Enrichment and Pathway
Analysis.
e functional enrichment tool DAVID (DAVID Bioinformatics Resources, https://david.ncifcrf.gov/, ver. 6.8) [45] was used to calculate both the Kyoto Encyclopedia of Genes and Genomes pathway and GO biological processes enrichment.

Compound-Compound Target Network Analysis.
is network contained 238 nodes (161 compound target nodes and 77 active compound nodes), as shown in Figure 3. On the one hand, there were many compound targets corresponding to multiple components in the network, suggesting that different components often have common targets. e chief of all compounds is quercetin, which has the widest regulating range; in other words, the targets of quercetin are the maximum. On the other hand, some targets can be modulated by only one compound (peripheral nodes), such as TNF and CYP1A2 targets, while PTGS2 target can be controlled by all 77 compounds, which may be the pivotal targets in DCHD. us, from the network, we can have a general observation on the relationships between active compounds and targets from the compound-compound target network. e targets of all herbal composition in DCHD correspond to a variety of diseases besides T2DM, which can exert pharmacological effects on multiple diseases. erefore, this network also fully reflects the characteristics of multicomponent-multitarget-multidisease of TCM formula.

T2DM PPI Network.
e 161 targets of DCHD were corresponding to 200 targets of T2DM, and 38 repeat important targets were obtained (as shown in Table 3). In order to illuminate the significance of degree in compound targets, we created a PPI network about the relationship of the common targets between compounds and T2DM.
is network was composed of 38 nodes and 349 edges. e more the edges between two nodes are, the greater the degree of Evidence-Based Complementary and Alternative Medicine  en, a topological analysis was carried out to further screen the core targets with the critical effect ( Figure 4). From the figure, No. 1 represented the interaction of 38 important targets, including the 18 yellow nodes representing the core targets obtained in the first topology analysis and the remaining 20 representing noncore targets. In the first topological analysis, based on the criteria of DC ≥ 20, CC ≥ 0.685, and BC ≥ 0.011, 18 nodes and 148 edges (No. 2) were obtained, and among the 18 nodes, 7 yellow nodes represent the core targets obtained in the second topology analysis, while 11 blue nodes represent the noncore targets. After the second topological analysis, with the criteria of DC ≥ 26, CC ≥ 0.771, and BC ≥ 0.029, PPI network (No. 3) was finally obtained, containing 7 nodes marked in yellow and 21 edges, indicating that there were 7 core targets obtained after topological analysis, namely, CAT, interleukin-6 (IL-6), tumor necrosis factor (TNF), IL-1β, JUN, MAPK3, and tumor protein 53 (TP53). e correlations of these 7 core targets were all over 27, of which the highest degrees were CAT and IL-6, up to 31, followed by TNF (30), and then to IL-1β (28), and to JUN, MAPK3, and TP53 (all 27). We found that DC was positively correlated with CC and BC. e larger the target of CC or BC is, the greater its DC value will be, indicating the powerful synergic effect between this target and other targets, as well as the critical role in the treatment of T2DM. With regard to CAT, the expression of catalase in organism can be enhanced by drugs to strengthen the antioxidant state and regulate the metabolic homeostasis and then effectively control blood sugar [46]. e degrees of proinflammatory cytokines and chemokines such as IL-6 and TNF are the same as or close to that of the antioxidant factors. Experiments show that drugs can inhibit the expression of inflammatory factors such as IL-6, TNF, IL-1β, c-JUN, and MAPK3 to reduce the proinflammatory response and thus to treat T2DM [47]. TP53 is a transcription factor that regulates the cell cycle. When TP53 transcriptional activity is activated, cell growth may be inhibited or in apoptosis, thereby effectively preventing cancer, but the activity of TP53 is closely related to the metabolism of the body. When metabolic disorders such as T2DM occur in human body, TP53 will increase in order to regulate the expression of apoptosis, proinflammatory, and metabolic genes. erefore, TP53-mediated gene expression plays a vital role in the development of metabolic disorders in patients with T2DM [48,49].

Functional Analysis and Pathways of T2DM.
In order to further clarify the biological functions of target genes in vivo, we systematically analyzed the biological processes related to 38 compound targets using Cytoscape plug-in CLUE-GO in a hierarchical manner [50]. 25 biological processes related to T2DM were mined in DAVID for specific pathway enrichment analysis. In Figure 5, most genes were mainly involved in several biological processes, including oxidation-reduction process, positive regulation of transcription, DNA-templated, positive regulation of nitric biosynthetic process, response to hydrogen peroxide, activation of MAPK activity, positive regulation of smooth muscle proliferation, and negative regulation of apoptotic process. Undoubtedly, these biological processes were all involved in the pathogenesis of T2DM, so they may serve as a potential therapeutic mechanism for T2DM.
Spanidis et al. evaluated the oxidative stress of type 2 diabetic patients by examining redox status markers and found that a redox state marker, namely, the redox potential in static state, in patients with T2DM was markedly increased compared with that in normal individuals. erefore, the oxidative stress process may be one of the pathogenic mechanisms of diabetes mellitus [51]. Positive regulation of transcription can promote the synthesis of transcription factors such as TCF7L2 and mediate insulin secretion from islet β-cells, which is also a pathogenesis of T2DM [52]. Reynolds et al. found that decreased expression of endogenous nitric oxide synthase in obese T2DM patients may lead to a decrease in insulin-stimulated blood flow, thus positively regulating the synthesis of nitrogen-containing compounds may improve blood flow in patients with T2DM [53]. Koistinen et al. reported that the activation of p38MAPK enzyme in skeletal muscle of patients with T2DM was enhanced which is vital in the inflammatory process. erefore, inhibition of p38MAPK enzyme activity could remarkably reduce the expression of inflammatory factors Evidence-Based Complementary and Alternative Medicine [54]. High-density lipoprotein in patients with T2DM causes abnormal proliferation of vascular smooth muscle cells due to abnormal components and modifications and mediates proinflammatory and atherogenic effects [55]. Interestingly, the abovementioned biological processes were mainly involved in apoptosis and inflammation. In terms of apoptosis, oxidative stress is an important link in apoptosis [56], especially in pancreatic cells, and due to its low antioxidant enzyme activity, it is susceptible to oxidative stress [57,58]. MAPK is an important apoptosis signaling pathway. e apoptosis can be regulated by activating the MAPK cascade reaction pathways, in which p38MAPK can be activated by oxidative stress released by NADP oxidase, thereby promoting the occurrence of β-cell apoptosis [59]. erefore, oxidative stress leading to the apoptosis of pancreatic β-cells serves as an important pathogenesis of T2DM. Endothelial nitric oxide synthase participates in the secretion of insulinsensitizing substances in peripheral tissues, regulates insulin secretion and glucose tolerance, and inhibits β-cell apoptosis [60]. And the results of GO analysis also directly indicate that DCHD can inhibit the apoptosis of β-cells. With regard to inflammation, increase of p38MAPK enzyme activity and smooth muscle cell proliferation are both involved in the proinflammatory response in T2DM. e 38 core targets were imported into the DAVID, and 89 pathways were obtained. rough systematic review of relevant literature and summary of T2DM-related e yellow nodes represent the core targets, and the blue nodes represent the noncore targets. pathogenesis, 20 signal pathways closely related to T2DM were screened. To further integrate T2DM core targets, we finally detected two core pathways ( Figure 6), namely, TNF signaling pathway and PI3K-AKT signaling pathway, which many important genes participate in. For instance, inflammatory factors such as IL-6, IL-1β, TNF-α, and vascular cell adhesion molecule (VCAM-1) all get involved in inflammatory response via TNF signaling pathway. Endothelial nitric oxide synthase (NOS3), glycogen synthase kinase-3β (GSK-3β), peroxisome proliferator antigen receptor-c (PPAR-c), and B-cell lymphoma-2 (Bcl-2) get involved in the regulation of apoptosis through PI3K/AKT signaling pathway. erefore, the therapeutic mechanism of DCHD on T2DM mainly includes the following two modules: inflammation and cell apoptosis.
(1) Inflammation Module. T2DM is a low-grade inflammatory response disease [61]. e inflammatory reaction of T2DM is caused by metabolic disorders [62]. Due to overnutrition, adipocytes have accumulated too many nutrients in adipose tissues, liver, and skeletal muscle, which leads to the conversion of macrophages from antiinflammatory "M2" (alternate activation) to a proinflammatory "M1" (classical activation) phenotype, inducing the increase of inflammatory factors [63]. erefore, T2DM contains a large number of inflammatory factors triggering cascade reactions of inflammatory signals. is proinflammatory state in turn destructs the systemic insulin sensitivity and glucose homeostasis. As shown in Figure 6, TNFR1 is a member of TNF superfamily and is one of the main receptors of TNF. TNFR1 plays an important role in the positive regulation of NF-κB signaling pathway mediated by inflammatory factors such as TNF-α and IL-1β. When there is no inflammatory factor stimulation in human body, NF-κB binds to its inhibitor IĸBα in the cytoplasm when NF-κB is not activated. For T2DM patients, inflammatory factors, growth factors, or chemokines can be activated in the body. In the presence of NF-κB stimulation, inflammatory factors bind to TNFR1, and IKKβ complexes are activated by NF-κB-induced kinase phosphorylation, resulting in phosphorylation of IĸBα at the sites of Ser32 and Ser36, followed by degradation of the ubiquitin-proteasome pathway.
(2) Apoptosis Module. e fundamental pathophysiological mechanism of T2DM is insulin resistance, which results in islet β-cell dysfunction and, in extreme cases, apoptosis of β-cells. erefore, islet β-cell apoptosis serves as the main pathogenic feature of T2DM. Increasing evidence demonstrates that the PI3K/Akt signaling pathway plays a crucial role in regulating β-cell apoptosis [68]. PI3K is a class of phosphorylated phosphatidylinositol lipid kinase in the PI3K/Akt signaling pathway. AKT is a protein-serine/ threonine kinase that is activated by the recruitment of phosphoinositide to the plasma membrane, also known as protein kinase B. According to the difference of serine/ threonine residues, AKT is divided into three subtypes: AKT1, AKT2, and AKT3. AKT2 is mainly expressed in insulin-sensitive tissues such as skeletal muscle, adipose tissue, and liver. erefore, the occurrence and development "Y-axis" shows significantly enriched "biological process" related to target genes after GO analysis; "X-axis" represents the number of targets in −log10 (P value).
of AKT2 and T2DM are closely related. As shown in Figure 7, the process by which PI3K/Akt signaling pathway mediates islet β-cell apoptosis can be transduced by receptor tyrosine kinase (RTK) and TLR2 receptors. e first is the largest enzyme-linked receptor, which is both an enzyme and a cell surface receptor for cytokines, hormones including insulin, and polypeptide growth factors. e insulin receptor tyrosine kinase (IRTK) in RTKs is a transmembrane receptor composed of two extracellular α-subunits that bind to disulfides and two transmembrane β-subunits [69]. In insulin-sensitive tissues such as liver, adipose tissue, and muscle, insulin binds to the α-subunit of the IRTK, causing a conformational change in the RTK, thereby enhancing transphosphorylation between the transmembrane β-subunits further enhances the phosphokinase activity of the RTK, which in turn phosphorylates the insulin receptor substrate (IRS-1). IRS-1 successively combines with PI3K regulatory subunit p85 and catalytic subunit p110, and the activated PI3K produces the second messenger phospholipid inositol (3,4)-triphosphate to promote the activation of AKT. e other type is TLR2, which belongs to Toll-like receptor family and is a very conservative innate immune receptor that can recognize pathogen or injury-related molecular patterns, among which TLR2 is the member that recognizes pathogen-related molecular patterns most. When inflammatory factors in T2DM patients bind to TLR2 receptor, TLR2 activates Rac-1 [70], which belongs to the Rho family GTPase and is involved in the regulation of various cellular functions such as cytoskeletal reorganization, cell growth, and apoptosis [71]. Activated Rac-1 further activates PI3K [72] and AKT after PI3K rephosphorylation. After activation of AKT based on the above two methods, AKT phosphorylation can activate eNOS expression, and NO produced by eNOS can regulate systemic metabolism and insulin sensitivity [73]. In patients with T2DM, the insulin is relatively insufficient, and the NO produced via the PI3K/Akt signaling pathway is reduced, resulting in decreased insulin sensitivity, i.e., insulin resistance. Long-term insulin resistance results in islet β-cell dysfunction and ultimately β-cell apoptosis. AKT phosphorylation can also mediate glycogen synthesis by inhibiting GSK3 [62]. GSK3 is a serine/threonine kinase that inhibits glycogen synthesis by phosphorylating glycogen synthase (GS), destroying glucose homeostasis, facilitating the formation of insulin resistance, and possessing antiproliferation and β-cell apoptosis activity. Early studies have also shown that insulin inhibits GSK3 activity through phosphorylation induced via the PI3K/AKT signaling pathway and promotes glycogen synthesis and glucose utilization, thereby inhibiting apoptosis in β-cell cells [74]. In addition to the PI3K/Akt signaling pathway mentioned above, the TNF signaling pathway is also involved in the transduction of T2DM islet β-cell apoptosis. TNFR2 is a type II receptor of TNF-α in TNF signaling pathway, promoting cell migration, regeneration, and proliferation. TNFR2 activates JNK through its domain III binding to c-Jun-N-terminal kinase-(JNK-) specific signaling molecule AIP1. JNK is an important intracellular signal transduction pathway [75]. JNK activation promotes the expression of its downstream target gene c-JUN, which mediates the apoptosis of β-cells [76]. Targets for the regulation of β-cell apoptosis downstream of the PI3K/AKT signaling pathway include Bcl-2, TNF, and PPAR-c. Bcl-2 is one of the oncogenes closely related to apoptosis [77]. A common feature of Bcl-2 family proteins is the presence of short conserved sequences consisting of 20 homologous (BH) motifs. ere are three types of Bcl-2 proteins: prosurvival proteins, proapoptotic protein Bcl-2 homologous protein 3-(BH3-), and downstream multidomain proapoptotic effector proteins [78]. Many studies have shown that the intrinsic pathway of apoptosis is controlled by Bcl-2 family proteins present in the endoplasmic reticulum, mitochondria, and nuclear membrane [79][80][81]. erefore, the occurrence of β-cell apoptosis is caused by the concentration of proapoptotic Bcl-2 protein in the β-cell exceeding the concentration of antiapoptotic protein on the mitochondrial membrane of the intrinsic pathway [82]. TNF is one of the cytokines involved in systemic inflammation produced by activated macrophages, and TNF-α-mediated inflammatory responses initiate the onset of islet β-cell apoptosis [83]. PPAR-c is an important cell differentiation transcription factor, mainly expressed in adipose and skeletal muscle tissues. Activation of PPAR-c in such mature cells can induce the expression of genes involved in the insulin signaling cascade, thereby improving insulin sensitivity and islet structure, and increase β-cells, and therefore, PPAR-c has an effect of inhibiting β-cell apoptosis [84]. In summary, T2DM is a glucose metabolic disease characterized by β-cell apoptosis. Promotion of anti-β-cell apoptosis targets including Bcl-2 prosurvival protein and PPAR-c, and inhibition of other pro-β-cell apoptosis targets such as TNF can inhibit β-cell apoptosis, thereby effectively controlling the development of T2DM.
In the present study, by network pharmacology, we deeply analyzed the two major pathological mechanisms of T2DM mediated by inflammation and apoptosis from the corresponding pathways of T2DM targets. Taking DCHD as an example, we explored the therapeutic effect of drugs on T2DM by blocking the corresponding inflammatory or apoptotic pathway leading to T2DM, which is of great significance in guiding the treatment of T2DM. However, the results obtained in this study have not been experimentally verified. erefore, further experimental validation to increase the accuracy and feasibility of the research results is warranted.

Conclusion
As a traditionally prepared formula, DCHD is an important complementary and alternative medicine for the treatment of T2DM and has also been widely used in the treatment of T2DM in modern clinical practice. In this study, we attempted to clarify the relevant targets and related pathways for the treatment of T2DM with DCHD by integrating systematics, pharmacology, and genomics. Our research shows that the eight herbs in DCHD play a synergistic effect to some extent, mainly through the inflammatory signaling pathway represented by TNF signaling pathway and the apoptotic signaling pathway represented by PI3K/Akt signaling pathway. e targets in these two pathways may be the targets of T2DM. To sum up, network pharmacology established in this study preliminarily reveals the modern application value of TCM formula in multitarget treatment for related diseases at the molecular level and also provides a paradigm for studying multitarget treatment of diseases, which is of great practical significance.

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

Conflicts of Interest
e authors declare that there are no conflicts of interest with respect to this study.

Supplementary Materials
1ingredients: the 77 potential Da-Chai-Hu ingredients obtained through six principles of screening. 2Drug-related targets: related targets obtained for each drug ingredient. 3Drug-targets: drug targets based on the total drug targets obtained in 2Drug-related targets, the duplicates are deleted, and the corresponding specifications are found for each target obtained. 4Disease-targets: all the targets related to T2DM, and the top 200 targets taken in order from the largest to the smallest according to the inference score. 5Common targets of disease and drug: a duplicate of the drug's total targets and disease targets, which is a common target for drugs and diseases. 6Topology: screened twice by "degree, closeness centrality, and betweeness" to get the final 7 core targets. 7GO-BP: 25 biological processes related to T2DM mined in DAVID for specific pathway enrichment analysis. 8 PPI: the 161 targets of DCHD corresponding to 200 targets of T2DM, and 38 repeat important targets obtained. In order to illuminate the significance of degree in compound targets, we created a PPI network about the relationship of the common targets between compounds and T2DM. is network was composed of 38 nodes and 349 edges. C-T.cys: Targets-compounds network. Figure 3. D-C.cys: herbs-compounds network. Figure 2 ppi.cys: interrelationship between 38 targets. Figure 4. T-P.cys: targetpathway network. Figure 6. (Supplementary Materials)