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

Authors: Beida Ren; Ling Tan; Yiliang Xiong; Wenting Ji; Jie Mu; Yuying Pei; Fafeng Cheng; Xueqian Wang; Qingguo Wang
Journal: Evidence-Based Complementary and Alternative Medicine
 (2020)

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## Abstract

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.


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## Body

## 1. 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. The 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]. The 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. The 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 first-line hypoglycemic drug, metformin, there are side effects including lactic acidosis and digestive disorders [11, 12]. Therefore, 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. Therefore, 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 “xiāo kĕ (wasting-thirst, 消渴)” and “splenic pure heat (pí dān, 脾瘅)” in TCM field. The 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 xiāo 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 xiāo 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 liver-stomach heat pattern and its typical formula is Da-Chai-Hu Decoction (Dà Chái Hú Tāng, DCHD). The 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 xiāo kĕ itself resolves. The 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. Therefore, 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. Through 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]. Therefore, 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).Figure 1
Flowchart of the systems pharmacology of DCHD in the treatment of D2TM.


## 2. Materials and Methods

### 2.1. Data Preparation

#### 2.1.1. Components of Each Herb in DCHD

Seventy-seven active components obtained from the eight herbs in DCHD were screened through a well-rounded literature search and the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP,http://lsp.nwu.edu.cn/tcmsp.php) [28]. As a bran-new TCM research platform and database based on systematic pharmacology, TCMSP contains comprehensive contents, including active components, key absorption, distribution, metabolism, excretion (ADME) properties, drug-likeness (DL), active targets, pathways involved, and related diseases of all herbs included in Chinese Pharmacopoeia. From the TCMSP database, we obtained a total of 349 compounds, including 143 components in Radix Bupleuri (RB), 138 in Scutellariae Radix (SR), 119 in Radix Paeoniae Rubra (RPR), 116 in Arum Ternatum Thunb (ATT), 65 in Aurantii Fructus Immaturus (AFI), 92 in Radix et Rhizoma Rhei (RERR), 265 in Zingiber Officinale Roscoe, (ZOR), and 134 in Jujubae Fructus (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 in Scutellariae Radix, 4 in Radix Paeoniae Rubra, 4 in Arum Ternatum Thunb, 16 in Aurantii Fructus Immaturus, 5 in Radix et Rhizoma Rhei, 1 in Zingiber Officinale Roscoe, and 14 in Jujubae Fructus (as shown in Figure 2).Table 1
72 potential compounds of Da-Chai-Hu Decoction and their network parameters.
Mol ID
Molecule name
Structure
MW
AlogP
Hdon
Hacc
OB (%)
DL
MOL013187
Cubebin
356.4
3.19
1
6
57.13
0.64
MOL004598
3,5,6,7-Tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone
432.46
2.54
0
9
31.97
0.59
MOL004624
Longikaurin A
348.48
1.16
3
5
47.72
0.53
MOL004609
Areapillin
360.34
2.29
3
8
48.96
0.41
MOL000354
Isorhamnetin
316.28
1.76
4
7
49.6
0.31
MOL000490
Petunidin
317.29
1.65
5
7
30.05
0.31
MOL000098
Quercetin
302.25
1.5
5
7
46.43
0.28
MOL000422
Kaempferol
286.25
1.77
4
6
41.88
0.24
MOL001458
Coptisine
320.34
3.25
0
4
30.67
0.86
MOL002897
Epiberberine
336.39
3.45
0
4
43.09
0.78
MOL002909
5,7,2,5-Tetrahydroxy-8,6-dimethoxyflavone
376.34
2.02
4
9
33.82
0.45
MOL002934
Neobaicalein
374.37
2.54
2
8
104.34
0.44
MOL002927
Skullcapflavone II
374.37
2.54
2
8
69.51
0.44
MOL012266
Rivularin
344.34
2.55
2
7
37.94
0.37
MOL000552
5,2′-Dihydroxy-6,7,8-trimethoxyflavone
344.34
2.55
2
7
31.71
0.35
MOL002915
Salvigenin
328.34
2.82
1
6
49.07
0.33
MOL002917
5,2′,6′-Trihydroxy-7,8-dimethoxyflavone
330.31
2.3
3
7
45.05
0.33
MOL002932
Panicolin
314.31
2.57
2
6
76.26
0.29
MOL012245
5,7,4′-Trihydroxy-6-methoxyflavanone
302.3
2.28
3
6
36.63
0.27
MOL002933
5,7,4′-Trihydroxy-8-methoxyflavone
300.28
2.32
3
6
36.56
0.27
MOL012246
5,7,4′-Trihydroxy-8-methoxyflavanone
302.3
2.28
3
6
74.24
0.26
MOL008206
Moslosooflavone
298.31
2.84
1
5
44.09
0.25
MOL002914
Eriodictyol (flavanone)
288.27
2.03
4
6
41.35
0.24
MOL002910
Carthamidin
288.27
2.03
4
6
41.15
0.24
MOL002925
5,7,2′,6′-Tetrahydroxyflavone
286.25
2.07
4
6
37.01
0.24
MOL001689
Acacetin
284.28
2.59
2
5
34.97
0.24
MOL002937
Dihydrooroxylin
286.3
2.55
2
5
66.06
0.23
MOL002928
Oroxylin A
284.28
2.59
2
5
41.37
0.23
MOL000173
Wogonin
284.28
2.59
2
5
30.68
0.23
MOL002913
Dihydrobaicalin_qt
272.27
2.3
3
5
40.04
0.21
MOL000525
Norwogonin
270.25
2.33
3
5
39.4
0.21
MOL002714
Baicalein
270.25
2.33
3
5
33.52
0.21
MOL000228
(2R)-7-Hydroxy-5-methoxy-2-phenylchroman-4-one
270.3
2.82
1
4
55.23
0.2
MOL001002
Ellagic acid
302.2
1.48
4
8
43.06
0.43
MOL001918
Paeoniflorgenone
318.35
0.79
1
6
87.59
0.37
MOL006992
(2R,3R)-4-Methoxyl-distylin
318.3
1.89
4
7
59.98
0.3
MOL002714
Baicalein
270.25
2.33
3
5
33.52
0.21
MOL002670
Cavidine
353.45
3.72
0
5
35.64
0.81
MOL006957
(3S,6S)-3-(Benzyl)-6-(4-hydroxybenzyl)piperazine-2,5-quinone
310.38
2.15
3
5
46.89
0.27
MOL006967
Beta-D-Ribofuranoside, xanthine-9
284.26
–1.29
5
9
44.72
0.21
MOL002714
Baicalein
270.25
2.33
3
5
33.52
0.21
MOL005828
Nobiletin
402.43
3.04
0
8
61.67
0.52
MOL001803
Sinensetin
372.4
3.06
0
7
50.56
0.45
MOL013277
Isosinensetin
372.4
3.06
0
7
51.15
0.44
MOL009053
4-[(2S,3R)-5-[(E)-3-Hydroxyprop-1-enyl]-7-methoxy-3-methylol-2,3-dihydrobenzofuran-2-yl]-2-methoxy-phenol
358.42
2.16
3
6
50.76
0.39
MOL007879
Tetramethoxyluteolin
342.37
3.07
0
6
43.68
0.37
MOL013435
Poncimarin
330.41
2.74
0
5
63.62
0.35
MOL013436
Isoponcimarin
330.41
2.94
0
5
63.28
0.31
MOL013279
5,7,4′-Trimethylapigenin
312.34
3.09
0
5
39.83
0.3
MOL013430
Prangenin
286.3
2.49
0
5
43.6
0.29
MOL001798
Neohesperidin_qt
302.3
2.28
3
6
71.17
0.27
MOL005100
5,7-Dihydroxy-2-(3-hydroxy-4-methoxyphenyl)chroman-4-one
302.3
2.28
3
6
47.74
0.27
MOL000006
Luteolin
286.25
2.07
4
6
36.16
0.25
MOL002914
Eriodictyol (flavanone)
288.27
2.03
4
6
41.35
0.24
MOL005849
Didymin
286.3
2.55
2
5
38.55
0.24
MOL001941
Ammidin
270.3
3.65
0
4
34.55
0.22
MOL004328
Naringenin
272.27
2.3
3
5
59.29
0.21
MOL002235
Eupatin
360.34
1.99
3
8
50.8
0.41
MOL002268
Rhein
284.23
1.88
3
6
47.07
0.28
MOL000471
Aloe-emodin
270.25
1.67
3
5
83.38
0.24
MOL000096
(−)-Catechin
290.29
1.92
5
6
49.68
0.24
MOL002281
Toralactone
272.27
2.25
2
5
46.46
0.24
MOL006129
6-Methylgingediacetate2
394.56
4.55
0
6
48.73
0.32
MOL000787
Fumarine
353.4
2.95
0
6
59.26
0.83
MOL001454
Berberine
336.39
3.45
0
4
36.86
0.78
MOL004350
Ruvoside_qt
390.57
2.29
3
5
36.12
0.76
MOL012986
Jujubasaponin V_qt
472.78
4.65
2
4
36.99
0.63
MOL012946
Ziziphus saponin I_qt
472.78
4.39
2
4
32.69
0.62
MOL000627
Stepholidine
327.41
3.1
2
5
33.11
0.54
MOL012992
Mauritine D
342.46
1.62
2
6
89.13
0.45
MOL007213
Nuciferine
295.41
3.57
0
3
34.43
0.4
MOL012976
Coumestrol
268.23
3.01
2
5
32.49
0.34
MOL012921
Stepharine
297.38
1.82
1
4
31.55
0.33
MOL001522
(S)-coclaurine
285.37
2.83
3
4
42.35
0.24
MOL000098
Quercetin
302.25
1.5
5
7
46.43
0.28
MOL000492
(+)-Catechin
290.29
1.92
5
6
54.83
0.24
MOL000096
(−)-Catechin
290.29
1.92
5
6
49.68
0.24Figure 2
Herb-compound network.(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. The components with MV from 180 to 500 Dalton are perceived as most available in drug therapy (MW ≤ 500).(2) AlogP Prediction. The 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. The 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. The 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 in-house system, OBioavail1.1 [32], and the compounds with OB ≥ 30% were filtered for further analysis. The 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. The DL of the compounds was calculated by the Tanimoto coefficient defined as follows:(1)TA,B=A⋅BA2+B2−A⋅B.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). The average of all descriptors was calculated by Dragon and the compounds with DL ≥ 0.18 were selected [35].From the above, in order to obtain the core active components, the screening principle was defined as follows: MW ≤ 500, AlogP ≤5, Hdon ≤5, Hacc ≤10, OB ≥ 30%, and DL ≥ 0.18.


#### 2.1.2. 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 [27]. Eventually, 161 targets from DCHD were obtained without repeated targets of the same component (as shown in Table 2).Table 2
The Da-Chai-Hu Decoction-related target information.
UniProt ID
Target name
Gene name
Drug
P23219
Prostaglandin G/H synthase 1
PTGS1
RB/SR/RPR/ATT/RRER/AFI/JF
P00742
Coagulation factor Xa
F10
RB/SR/ATT/AFI/RRER/ZOR/JF
P35354
Prostaglandin G/H synthase 2
PTGS2
RB/SR/RPR/ATT/RRER/AFI/ZOR/JF
P07550
Beta-2 adrenergic receptor
ADRB2
RB/SR/ATT/AFI/JF
P08238
Heat-shock protein HSP 90
HSP90AB1
RB/SR/RPR/ATT/RRER/AFI/JF
P00734
Thrombin
F2
RB/SR/AFI/RRER/JF
Q99527
Estrogen receptor
GPER1
RB/SR/RPR/RRER/AFI/ZOR/JF
P10275
Androgen receptor
AR
RB/SR/RPR/ATT/AFI/RRER/JF
P08709
Coagulation factor VII
F7
RB/SR/ATT/AFI/RRER/JF
P22303
Acetylcholinesterase
ACHE
RB/SR/AFI/JF
P11388
DNA topoisomerase II
TOP2
RB/SR/ATT/AFI/RRER/JF
Q92731
Estrogen receptor-beta
ESTRB
RB/SR/AFI/RRER
P07477
Trypsin-1
PRSS1
RB/SR/RPR/ATT/RRER/AFI/JF
Q15596
Nuclear receptor coactivator 2
NCOA2
RB/SR/RPR/ATT/RRER/AFI/JF
P0DP23
Calmodulin
CALM
RB/SR/RPR/ATT/RRER/AFI/ZOR/JF
P11229
Muscarinic acetylcholine receptor M1
CHRM1
RB/SR/ATT/AFI/JF
P47869
Gamma-aminobutyric-acid receptor alpha-2 subunit
GABRA2
RB
P34903
Gamma-aminobutyric-acid receptor alpha-3 subunit
GABRA3
RB/JF
P08172
Muscarinic acetylcholine receptor M2
CHRM2
RB/JF
Q16445
Gamma-aminobutyric-acid receptor subunit alpha-6
GABRA6
RB
P35228
Nitric oxide synthase, inducible
NOS2
RB/SR/RPR/RRER/AFI/JF
Q14524
Sodium channel protein type 5 subunit alpha
SCN5A
RB/SR/ATT/RRER/AFI/JF
P18031
mRNA of protein-tyrosine phosphatase, nonreceptor type 1
PTPN1
RB/SR/AFI
P27487
Dipeptidyl peptidase IV
DPP4
RB/SR/RPR/ATT/RRER/AFI/JF
P01857
Ig gamma-1 chain C region
IGHG1
RB/SR/RRER
Q15788
Nuclear receptor coactivator 1
NCOA1
RB/SR/RPR/ATT/AFI/JF
P37231
Peroxisome proliferator-activated receptor-gamma
PPARG
RB/SR/AFI/JF
Q16539
Mitogen-activated protein kinase 14
MAPK14
RB/SR/AFI
P49841
Glycogen synthase kinase-3 beta
GSK3B
RB/SR/AFI
P24941
Cell division protein kinase 2
CDK2
RB/SR/AFI
P48736
Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit, gamma isoform
PIK3CG
RB/SR/RPR/ATT/RRER/AFI/JF
P17612
mRNA of PKA catalytic subunit C-alpha
PRKACA
RB/SR/RPR/ATT/RRER/AFI/JF
P11309
Proto-oncogene serine/threonine-protein kinase Pim-1
PIM1
RB/AFI
P20248
Cyclin-A2
CCNA2
RB
P11217
Glycogen phosphorylase, muscle form
PYGM
RB/SR/AFI
Q03181
Peroxisome proliferator-activated receptor delta
PPARD
RB/SR/RRER
O14757
Serine/threonine-protein kinase Chk1
CHEK1
RB/SR/AFI/RRER
P15121
Aldose reductase
AKR1B1
RB/RRER/JF
P29474
Nitric oxide synthase, endothelial
NOS3
RB/SR/AFI/JF
P14867
Gamma-aminobutyric acid receptor subunit alpha-1
GABRA1
RB/SR/RPR/AFI/JF
P27338
Amine oxidase (flavin-containing) B
MAOB
RB/SR/AFI/JF
P42262
Glutamate receptor 2
GRIA2
RB
P47989
Xanthine dehydrogenase/oxidase
XDH
RB/AFI/JF
P06401
Progesterone receptor
PGR
RB/RPR
P23975
Sodium-dependent noradrenaline transporter
SLC6A2
RB/JF
P35368
Alpha-1B adrenergic receptor
ADRA1B
RB/SR/ATT/AFI/JF
P10415
Apoptosis regulator Bcl-2
BCL2
RB/SR/RPR/ATT/AFI/JF
P01375
Tumor necrosis factor
TNF
RB/SR/AFI/RRER/JF
P05412
Transcription factor AP-1
JUN
RB/SR/AFI/RRER/JF
P45983
Mitogen-activated protein kinase 8
MAPK8
RB/AFI
P03956
Interstitial collagenase
MMP1
RB/SR/AFI/JF
P06493
Cell division control protein 2 homolog
CDC2
RB/SR/RPR/ATT/RRER/JF
P09601
Heme oxygenase 1
HMOX1
RB/AFI/JF
P08684
Cytochrome P450 3A4
CYP3A4
RB/JF
P05177
Cytochrome P450 1A2
CYP1A2
RB/SR/JF
P16581
E-selectin
SELE
RB/JF
P19320
Vascular cell adhesion protein 1
VCAM1
RB/JF
P09917
Arachidonate 5-lipoxygenase
ALOX5
RB/JF
P09211
Glutathione S-transferase P
GSTP1
RB/RPR/AFI/JF
P35869
Aryl hydrocarbon receptor
AHR
RB/SR/RPR/ATT/JF
P06213
Insulin receptor
INSR
RB/AFI/JF
Q08209
Serine/threonine-protein phosphatase 2B catalytic subunit alpha isoform
PPP3CA
RB
P09488
Glutathione S-transferase Mu 1
GSTM1
RB/RPR/JF
P28161
Glutathione S-transferase Mu 2
GSTM2
RB/RPR/JF
P42330
Aldo-keto reductase family 1 member C3
AKR1C3
RB
Q12809
Potassium voltage-gated channel subfamily H member 2
KCNH2
RB/SR/ATT/AFI/JF
P08254
Stromelysin-1
MMP3
RB/JF
P19793
Retinoic acid receptor RXR-alpha
RXRA
RB/SR/ATT/AFI/JF
P00533
Epidermal growth factor receptor
EGFR
RB/AFI/JF
P15692
Vascular endothelial growth factor A
VEGFA
RB/SR/ATT/RRER/AFI/JF
P00749
Urokinase-type plasminogen activator
PLAU
RB/JF
P08253
72 kDa type IV collagenase
MMP2
RB/RPR/AFI/JF
P28482
Mitogen-activated protein kinase 1
MAPK1
RB/SR/AFI/JF
P01133
Proepidermal growth factor
EGF
RB/JF
P06400
Retinoblastoma-associated protein
RB1
RB/AFI/JF
P05231
Interleukin-6
IL6
RB/SR/AFI/JF
P04637
Cellular tumor antigen p53
TP53
RB/SR/RPR/ATT/RRER/AFI/JF
P16435
NADPH-cytochrome P450 reductase
POR
RB/JF
P11926
Ornithine decarboxylase
ODC1
RB/JF
P11387
DNA topoisomerase 1
TOP1
RB/AFI/JF
P00441
Superoxide dismutase (Cu-Zn)
SOD1
RB/AFI/JF
P11021
78 kDa glucose-regulated protein
HSPA5
RB/JF
Q13085
Acetyl-CoA carboxylase 1
ACACA
RB/JF
P13726
Tissue factor
F3
RB/JF
P17302
Gap junction alpha-1 protein
GJA1
RB/JF
P01584
Interleukin-1 beta
IL1B
RB/RRER/JF
Q99616
C-C motif chemokine 2
CCL13
RB/SR/JF
P43115
Prostaglandin E2 receptor EP3 subtype
PTGER3
RB/SR/JF
P49888
Estrogen sulfotransferase
SULT1E1
RB/SR/JF
O43451
Maltase-glucoamylase, intestinal
MGAM
RB/SR/JF
P60568
Interleukin-2
IL2
RB/AFI/JF
P00750
Tissue-type plasminogen activator
PLAT
RB/JF
P07204
Thrombomodulin
THBD
RB/JF
P02452
Collagen alpha-1(I) chain
COL1A1
RB/JF
P01579
Interferon-gamma
IFNG
RB/AFI/JF
P05164
Myeloperoxidase
MPO
RB/SR/RPR/ATT/JF
P15559
NAD(P)H dehydrogenase [quinone] 1
NQO1
RB/JF
P02461
Collagen alpha-1(III) chain
COL3A1
RB/JF
P15309
Prostatic acid phosphatase
ACPP
RB/JF
P07339
Cathepsin D
CTSD
RB/JF
P27169
Serum paraoxonase/arylesterase 1
PON1
RB/JF
Q9Y233
cAMP and cAMP-inhibited cGMP 3′,5′-cyclic phosphodiesterase 10A
PDE10A
SR/ATT/JF
Q12791
Calcium-activated potassium channel subunit alpha 1
KCNMA1
SR/AFI
P35968
Vascular endothelial growth factor receptor 2
KDR
SR/RRER/JF
P54289
Voltage-dependent calcium channel subunit alpha-2/delta-1
CACNA2D1
SR
Q14432
CGMP-inhibited 3′,5′-cyclic phosphodiesterase A
PDE3A
SR/RPR/ATT/AFI/JF
P61925
cAMP-dependent protein kinase inhibitor alpha
PKIA
SR/RRER
P50613
Cell division protein kinase 7
CDK7
SR
P11712
Cytochrome P450 2C9
CYP2C9
SR
P99999
Cytochrome c
CYCS
SR/RPR/ATT
Q9GZT9
Egl nine homolog 1
EGLN1
SR/RPR/ATT
P00918
Carbonic anhydrase II
CA2
SR/AFI/ATT
P36544
Neuronal acetylcholine receptor protein, alpha-7 chain
CHRNA7
SR/JF
P49327
Fatty acid synthase
FASN
SR/AFI/RRER/JF
P21728
Dopamine D1 receptor
DRD1
SR/ATT/JF
P20309
Muscarinic acetylcholine receptor M3
CHRM3
SR/ATT/JF
P35348
Alpha-1A adrenergic receptor
ADRA1A
SR/JF
Q01959
Sodium-dependent dopamine transporter
SLC6A3
SR/JF
P31645
Sodium-dependent serotonin transporter
SLC6A4
SR/ATT/JF
P08263
Glutathione S-transferase A1
GSTA1
RPR
P09210
Glutathione S-transferase A2
GSTA2
RPR
P08588
Beta-1 adrenergic receptor
ADRB1
ATT
P08912
Muscarinic acetylcholine receptor M5
CHRM5
ATT/JF
P46098
5-Hydroxytryptamine receptor 3A
HTR3A
ATT/JF
P18825
Alpha-2C adrenergic receptor
ADRA2C
ATT/AFI/JF
P08173
Muscarinic acetylcholine receptor M4
CHRM4
ATT/JF
P41143
Delta-type opioid receptor
OPRD1
ATT/JF
P28223
5-Hydroxytryptamine 2A receptor
HTR2A
ATT/JF
P28335
5-Hydroxytryptamine 2C receptor
HTR2C
ATT/JF
P25100
Alpha-1D adrenergic receptor
ADRA1D
ATT/JF
P35372
Mu-type opioid receptor
OPRM1
ATT/JF
P28702
Retinoic acid receptor RXR-beta
RXRB
ATT
P00491
Purine nucleoside phosphorylase
PNP
ATT
P47712
Cytosolic phospholipase A2
PLA2G4A
AFI
Q09428
Sulfonylurea receptor 1
ABCC8
AFI
P56817
Beta-secretase
BACE1
AFI
P11802
Cell division protein kinase 4
CDK4
AFI
P05067
Amyloid beta A4 protein
APP
AFI
P55210
Caspase-7
CASP7
AFI
P08581
Hepatocyte growth factor receptor
MET
AFI
P27361
Mitogen-activated protein kinase 3
MAPK3
AFI
P01130
Low-density lipoprotein receptor
LDLR
AFI
P04040
Catalase
CAT
AFI/JF
P04035
3-Hydroxy-3-methylglutaryl-coenzyme A reductase
HMGCR
AFI
P00390
Glutathione reductase, mitochondrial
GSR
AFI
P33527
Multidrug resistance-associated protein 1
ABCC1
AFI
Q04828
Aldo-keto reductase family 1 member C1
AKR1C1
AFI
P17174
Aspartate aminotransferase, cytoplasmic
GOT1
AFI
P80404
4-Aminobutyrate aminotransferase, mitochondrial
ABAT
AFI
P35610
Sterol O-acyltransferase 1
SOAT1
AFI
Q13698
Voltage-dependent L-type calcium channel subunit alpha-1S
CACNA1S
JF
P27815
Type IV phosphodiesterase
PDE4A
JF
P08235
Mineralocorticoid receptor
NR3C2
JF
P21918
D(1B) dopamine receptor
DRD5
JF
P21917
D(4) dopamine receptor
DRD4
JF
P18089
Alpha-2B adrenergic receptor
ADRA2B
JF
Q15822
Neuronal acetylcholine receptor subunit alpha-2
CHRNA2
JF
P14416
D(2) dopamine receptor
DRD2
JF
P20813
Cytochrome P450 2B6
CYP2B6
JF
P08913
Alpha-2A adrenergic receptor
ADRA2A
JF
O75469
Pregnane X receptor
NR1I2
AFI


#### 2.1.3. 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 gene-centric data from 125 web sources, including genomic, transcriptomic, proteomic, genetic, clinical, and functional information.


#### 2.1.4. Protein-Protein Interaction Data

All the protein-protein 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. The 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].


### 2.2. Network Construction

#### 2.2.1. Network Construction

The 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.


#### 2.2.2. Network Topological Features

There 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]. The levels of the aforementioned three parameters reflect the importance of a node in the network. The higher the value of the parameter is, the more important the node becomes.


#### 2.2.3. Gene Ontology (GO) Enrichment and Pathway Analysis

The 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.


## 2.1. Data Preparation

### 2.1.1. Components of Each Herb in DCHD

Seventy-seven active components obtained from the eight herbs in DCHD were screened through a well-rounded literature search and the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP,http://lsp.nwu.edu.cn/tcmsp.php) [28]. As a bran-new TCM research platform and database based on systematic pharmacology, TCMSP contains comprehensive contents, including active components, key absorption, distribution, metabolism, excretion (ADME) properties, drug-likeness (DL), active targets, pathways involved, and related diseases of all herbs included in Chinese Pharmacopoeia. From the TCMSP database, we obtained a total of 349 compounds, including 143 components in Radix Bupleuri (RB), 138 in Scutellariae Radix (SR), 119 in Radix Paeoniae Rubra (RPR), 116 in Arum Ternatum Thunb (ATT), 65 in Aurantii Fructus Immaturus (AFI), 92 in Radix et Rhizoma Rhei (RERR), 265 in Zingiber Officinale Roscoe, (ZOR), and 134 in Jujubae Fructus (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 in Scutellariae Radix, 4 in Radix Paeoniae Rubra, 4 in Arum Ternatum Thunb, 16 in Aurantii Fructus Immaturus, 5 in Radix et Rhizoma Rhei, 1 in Zingiber Officinale Roscoe, and 14 in Jujubae Fructus (as shown in Figure 2).Table 1
72 potential compounds of Da-Chai-Hu Decoction and their network parameters.
Mol ID
Molecule name
Structure
MW
AlogP
Hdon
Hacc
OB (%)
DL
MOL013187
Cubebin
356.4
3.19
1
6
57.13
0.64
MOL004598
3,5,6,7-Tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone
432.46
2.54
0
9
31.97
0.59
MOL004624
Longikaurin A
348.48
1.16
3
5
47.72
0.53
MOL004609
Areapillin
360.34
2.29
3
8
48.96
0.41
MOL000354
Isorhamnetin
316.28
1.76
4
7
49.6
0.31
MOL000490
Petunidin
317.29
1.65
5
7
30.05
0.31
MOL000098
Quercetin
302.25
1.5
5
7
46.43
0.28
MOL000422
Kaempferol
286.25
1.77
4
6
41.88
0.24
MOL001458
Coptisine
320.34
3.25
0
4
30.67
0.86
MOL002897
Epiberberine
336.39
3.45
0
4
43.09
0.78
MOL002909
5,7,2,5-Tetrahydroxy-8,6-dimethoxyflavone
376.34
2.02
4
9
33.82
0.45
MOL002934
Neobaicalein
374.37
2.54
2
8
104.34
0.44
MOL002927
Skullcapflavone II
374.37
2.54
2
8
69.51
0.44
MOL012266
Rivularin
344.34
2.55
2
7
37.94
0.37
MOL000552
5,2′-Dihydroxy-6,7,8-trimethoxyflavone
344.34
2.55
2
7
31.71
0.35
MOL002915
Salvigenin
328.34
2.82
1
6
49.07
0.33
MOL002917
5,2′,6′-Trihydroxy-7,8-dimethoxyflavone
330.31
2.3
3
7
45.05
0.33
MOL002932
Panicolin
314.31
2.57
2
6
76.26
0.29
MOL012245
5,7,4′-Trihydroxy-6-methoxyflavanone
302.3
2.28
3
6
36.63
0.27
MOL002933
5,7,4′-Trihydroxy-8-methoxyflavone
300.28
2.32
3
6
36.56
0.27
MOL012246
5,7,4′-Trihydroxy-8-methoxyflavanone
302.3
2.28
3
6
74.24
0.26
MOL008206
Moslosooflavone
298.31
2.84
1
5
44.09
0.25
MOL002914
Eriodictyol (flavanone)
288.27
2.03
4
6
41.35
0.24
MOL002910
Carthamidin
288.27
2.03
4
6
41.15
0.24
MOL002925
5,7,2′,6′-Tetrahydroxyflavone
286.25
2.07
4
6
37.01
0.24
MOL001689
Acacetin
284.28
2.59
2
5
34.97
0.24
MOL002937
Dihydrooroxylin
286.3
2.55
2
5
66.06
0.23
MOL002928
Oroxylin A
284.28
2.59
2
5
41.37
0.23
MOL000173
Wogonin
284.28
2.59
2
5
30.68
0.23
MOL002913
Dihydrobaicalin_qt
272.27
2.3
3
5
40.04
0.21
MOL000525
Norwogonin
270.25
2.33
3
5
39.4
0.21
MOL002714
Baicalein
270.25
2.33
3
5
33.52
0.21
MOL000228
(2R)-7-Hydroxy-5-methoxy-2-phenylchroman-4-one
270.3
2.82
1
4
55.23
0.2
MOL001002
Ellagic acid
302.2
1.48
4
8
43.06
0.43
MOL001918
Paeoniflorgenone
318.35
0.79
1
6
87.59
0.37
MOL006992
(2R,3R)-4-Methoxyl-distylin
318.3
1.89
4
7
59.98
0.3
MOL002714
Baicalein
270.25
2.33
3
5
33.52
0.21
MOL002670
Cavidine
353.45
3.72
0
5
35.64
0.81
MOL006957
(3S,6S)-3-(Benzyl)-6-(4-hydroxybenzyl)piperazine-2,5-quinone
310.38
2.15
3
5
46.89
0.27
MOL006967
Beta-D-Ribofuranoside, xanthine-9
284.26
–1.29
5
9
44.72
0.21
MOL002714
Baicalein
270.25
2.33
3
5
33.52
0.21
MOL005828
Nobiletin
402.43
3.04
0
8
61.67
0.52
MOL001803
Sinensetin
372.4
3.06
0
7
50.56
0.45
MOL013277
Isosinensetin
372.4
3.06
0
7
51.15
0.44
MOL009053
4-[(2S,3R)-5-[(E)-3-Hydroxyprop-1-enyl]-7-methoxy-3-methylol-2,3-dihydrobenzofuran-2-yl]-2-methoxy-phenol
358.42
2.16
3
6
50.76
0.39
MOL007879
Tetramethoxyluteolin
342.37
3.07
0
6
43.68
0.37
MOL013435
Poncimarin
330.41
2.74
0
5
63.62
0.35
MOL013436
Isoponcimarin
330.41
2.94
0
5
63.28
0.31
MOL013279
5,7,4′-Trimethylapigenin
312.34
3.09
0
5
39.83
0.3
MOL013430
Prangenin
286.3
2.49
0
5
43.6
0.29
MOL001798
Neohesperidin_qt
302.3
2.28
3
6
71.17
0.27
MOL005100
5,7-Dihydroxy-2-(3-hydroxy-4-methoxyphenyl)chroman-4-one
302.3
2.28
3
6
47.74
0.27
MOL000006
Luteolin
286.25
2.07
4
6
36.16
0.25
MOL002914
Eriodictyol (flavanone)
288.27
2.03
4
6
41.35
0.24
MOL005849
Didymin
286.3
2.55
2
5
38.55
0.24
MOL001941
Ammidin
270.3
3.65
0
4
34.55
0.22
MOL004328
Naringenin
272.27
2.3
3
5
59.29
0.21
MOL002235
Eupatin
360.34
1.99
3
8
50.8
0.41
MOL002268
Rhein
284.23
1.88
3
6
47.07
0.28
MOL000471
Aloe-emodin
270.25
1.67
3
5
83.38
0.24
MOL000096
(−)-Catechin
290.29
1.92
5
6
49.68
0.24
MOL002281
Toralactone
272.27
2.25
2
5
46.46
0.24
MOL006129
6-Methylgingediacetate2
394.56
4.55
0
6
48.73
0.32
MOL000787
Fumarine
353.4
2.95
0
6
59.26
0.83
MOL001454
Berberine
336.39
3.45
0
4
36.86
0.78
MOL004350
Ruvoside_qt
390.57
2.29
3
5
36.12
0.76
MOL012986
Jujubasaponin V_qt
472.78
4.65
2
4
36.99
0.63
MOL012946
Ziziphus saponin I_qt
472.78
4.39
2
4
32.69
0.62
MOL000627
Stepholidine
327.41
3.1
2
5
33.11
0.54
MOL012992
Mauritine D
342.46
1.62
2
6
89.13
0.45
MOL007213
Nuciferine
295.41
3.57
0
3
34.43
0.4
MOL012976
Coumestrol
268.23
3.01
2
5
32.49
0.34
MOL012921
Stepharine
297.38
1.82
1
4
31.55
0.33
MOL001522
(S)-coclaurine
285.37
2.83
3
4
42.35
0.24
MOL000098
Quercetin
302.25
1.5
5
7
46.43
0.28
MOL000492
(+)-Catechin
290.29
1.92
5
6
54.83
0.24
MOL000096
(−)-Catechin
290.29
1.92
5
6
49.68
0.24Figure 2
Herb-compound network.(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. The components with MV from 180 to 500 Dalton are perceived as most available in drug therapy (MW ≤ 500).(2) AlogP Prediction. The 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. The 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. The 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 in-house system, OBioavail1.1 [32], and the compounds with OB ≥ 30% were filtered for further analysis. The 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. The DL of the compounds was calculated by the Tanimoto coefficient defined as follows:(1)TA,B=A⋅BA2+B2−A⋅B.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). The average of all descriptors was calculated by Dragon and the compounds with DL ≥ 0.18 were selected [35].From the above, in order to obtain the core active components, the screening principle was defined as follows: MW ≤ 500, AlogP ≤5, Hdon ≤5, Hacc ≤10, OB ≥ 30%, and DL ≥ 0.18.


### 2.1.2. 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 [27]. Eventually, 161 targets from DCHD were obtained without repeated targets of the same component (as shown in Table 2).Table 2
The Da-Chai-Hu Decoction-related target information.
UniProt ID
Target name
Gene name
Drug
P23219
Prostaglandin G/H synthase 1
PTGS1
RB/SR/RPR/ATT/RRER/AFI/JF
P00742
Coagulation factor Xa
F10
RB/SR/ATT/AFI/RRER/ZOR/JF
P35354
Prostaglandin G/H synthase 2
PTGS2
RB/SR/RPR/ATT/RRER/AFI/ZOR/JF
P07550
Beta-2 adrenergic receptor
ADRB2
RB/SR/ATT/AFI/JF
P08238
Heat-shock protein HSP 90
HSP90AB1
RB/SR/RPR/ATT/RRER/AFI/JF
P00734
Thrombin
F2
RB/SR/AFI/RRER/JF
Q99527
Estrogen receptor
GPER1
RB/SR/RPR/RRER/AFI/ZOR/JF
P10275
Androgen receptor
AR
RB/SR/RPR/ATT/AFI/RRER/JF
P08709
Coagulation factor VII
F7
RB/SR/ATT/AFI/RRER/JF
P22303
Acetylcholinesterase
ACHE
RB/SR/AFI/JF
P11388
DNA topoisomerase II
TOP2
RB/SR/ATT/AFI/RRER/JF
Q92731
Estrogen receptor-beta
ESTRB
RB/SR/AFI/RRER
P07477
Trypsin-1
PRSS1
RB/SR/RPR/ATT/RRER/AFI/JF
Q15596
Nuclear receptor coactivator 2
NCOA2
RB/SR/RPR/ATT/RRER/AFI/JF
P0DP23
Calmodulin
CALM
RB/SR/RPR/ATT/RRER/AFI/ZOR/JF
P11229
Muscarinic acetylcholine receptor M1
CHRM1
RB/SR/ATT/AFI/JF
P47869
Gamma-aminobutyric-acid receptor alpha-2 subunit
GABRA2
RB
P34903
Gamma-aminobutyric-acid receptor alpha-3 subunit
GABRA3
RB/JF
P08172
Muscarinic acetylcholine receptor M2
CHRM2
RB/JF
Q16445
Gamma-aminobutyric-acid receptor subunit alpha-6
GABRA6
RB
P35228
Nitric oxide synthase, inducible
NOS2
RB/SR/RPR/RRER/AFI/JF
Q14524
Sodium channel protein type 5 subunit alpha
SCN5A
RB/SR/ATT/RRER/AFI/JF
P18031
mRNA of protein-tyrosine phosphatase, nonreceptor type 1
PTPN1
RB/SR/AFI
P27487
Dipeptidyl peptidase IV
DPP4
RB/SR/RPR/ATT/RRER/AFI/JF
P01857
Ig gamma-1 chain C region
IGHG1
RB/SR/RRER
Q15788
Nuclear receptor coactivator 1
NCOA1
RB/SR/RPR/ATT/AFI/JF
P37231
Peroxisome proliferator-activated receptor-gamma
PPARG
RB/SR/AFI/JF
Q16539
Mitogen-activated protein kinase 14
MAPK14
RB/SR/AFI
P49841
Glycogen synthase kinase-3 beta
GSK3B
RB/SR/AFI
P24941
Cell division protein kinase 2
CDK2
RB/SR/AFI
P48736
Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit, gamma isoform
PIK3CG
RB/SR/RPR/ATT/RRER/AFI/JF
P17612
mRNA of PKA catalytic subunit C-alpha
PRKACA
RB/SR/RPR/ATT/RRER/AFI/JF
P11309
Proto-oncogene serine/threonine-protein kinase Pim-1
PIM1
RB/AFI
P20248
Cyclin-A2
CCNA2
RB
P11217
Glycogen phosphorylase, muscle form
PYGM
RB/SR/AFI
Q03181
Peroxisome proliferator-activated receptor delta
PPARD
RB/SR/RRER
O14757
Serine/threonine-protein kinase Chk1
CHEK1
RB/SR/AFI/RRER
P15121
Aldose reductase
AKR1B1
RB/RRER/JF
P29474
Nitric oxide synthase, endothelial
NOS3
RB/SR/AFI/JF
P14867
Gamma-aminobutyric acid receptor subunit alpha-1
GABRA1
RB/SR/RPR/AFI/JF
P27338
Amine oxidase (flavin-containing) B
MAOB
RB/SR/AFI/JF
P42262
Glutamate receptor 2
GRIA2
RB
P47989
Xanthine dehydrogenase/oxidase
XDH
RB/AFI/JF
P06401
Progesterone receptor
PGR
RB/RPR
P23975
Sodium-dependent noradrenaline transporter
SLC6A2
RB/JF
P35368
Alpha-1B adrenergic receptor
ADRA1B
RB/SR/ATT/AFI/JF
P10415
Apoptosis regulator Bcl-2
BCL2
RB/SR/RPR/ATT/AFI/JF
P01375
Tumor necrosis factor
TNF
RB/SR/AFI/RRER/JF
P05412
Transcription factor AP-1
JUN
RB/SR/AFI/RRER/JF
P45983
Mitogen-activated protein kinase 8
MAPK8
RB/AFI
P03956
Interstitial collagenase
MMP1
RB/SR/AFI/JF
P06493
Cell division control protein 2 homolog
CDC2
RB/SR/RPR/ATT/RRER/JF
P09601
Heme oxygenase 1
HMOX1
RB/AFI/JF
P08684
Cytochrome P450 3A4
CYP3A4
RB/JF
P05177
Cytochrome P450 1A2
CYP1A2
RB/SR/JF
P16581
E-selectin
SELE
RB/JF
P19320
Vascular cell adhesion protein 1
VCAM1
RB/JF
P09917
Arachidonate 5-lipoxygenase
ALOX5
RB/JF
P09211
Glutathione S-transferase P
GSTP1
RB/RPR/AFI/JF
P35869
Aryl hydrocarbon receptor
AHR
RB/SR/RPR/ATT/JF
P06213
Insulin receptor
INSR
RB/AFI/JF
Q08209
Serine/threonine-protein phosphatase 2B catalytic subunit alpha isoform
PPP3CA
RB
P09488
Glutathione S-transferase Mu 1
GSTM1
RB/RPR/JF
P28161
Glutathione S-transferase Mu 2
GSTM2
RB/RPR/JF
P42330
Aldo-keto reductase family 1 member C3
AKR1C3
RB
Q12809
Potassium voltage-gated channel subfamily H member 2
KCNH2
RB/SR/ATT/AFI/JF
P08254
Stromelysin-1
MMP3
RB/JF
P19793
Retinoic acid receptor RXR-alpha
RXRA
RB/SR/ATT/AFI/JF
P00533
Epidermal growth factor receptor
EGFR
RB/AFI/JF
P15692
Vascular endothelial growth factor A
VEGFA
RB/SR/ATT/RRER/AFI/JF
P00749
Urokinase-type plasminogen activator
PLAU
RB/JF
P08253
72 kDa type IV collagenase
MMP2
RB/RPR/AFI/JF
P28482
Mitogen-activated protein kinase 1
MAPK1
RB/SR/AFI/JF
P01133
Proepidermal growth factor
EGF
RB/JF
P06400
Retinoblastoma-associated protein
RB1
RB/AFI/JF
P05231
Interleukin-6
IL6
RB/SR/AFI/JF
P04637
Cellular tumor antigen p53
TP53
RB/SR/RPR/ATT/RRER/AFI/JF
P16435
NADPH-cytochrome P450 reductase
POR
RB/JF
P11926
Ornithine decarboxylase
ODC1
RB/JF
P11387
DNA topoisomerase 1
TOP1
RB/AFI/JF
P00441
Superoxide dismutase (Cu-Zn)
SOD1
RB/AFI/JF
P11021
78 kDa glucose-regulated protein
HSPA5
RB/JF
Q13085
Acetyl-CoA carboxylase 1
ACACA
RB/JF
P13726
Tissue factor
F3
RB/JF
P17302
Gap junction alpha-1 protein
GJA1
RB/JF
P01584
Interleukin-1 beta
IL1B
RB/RRER/JF
Q99616
C-C motif chemokine 2
CCL13
RB/SR/JF
P43115
Prostaglandin E2 receptor EP3 subtype
PTGER3
RB/SR/JF
P49888
Estrogen sulfotransferase
SULT1E1
RB/SR/JF
O43451
Maltase-glucoamylase, intestinal
MGAM
RB/SR/JF
P60568
Interleukin-2
IL2
RB/AFI/JF
P00750
Tissue-type plasminogen activator
PLAT
RB/JF
P07204
Thrombomodulin
THBD
RB/JF
P02452
Collagen alpha-1(I) chain
COL1A1
RB/JF
P01579
Interferon-gamma
IFNG
RB/AFI/JF
P05164
Myeloperoxidase
MPO
RB/SR/RPR/ATT/JF
P15559
NAD(P)H dehydrogenase [quinone] 1
NQO1
RB/JF
P02461
Collagen alpha-1(III) chain
COL3A1
RB/JF
P15309
Prostatic acid phosphatase
ACPP
RB/JF
P07339
Cathepsin D
CTSD
RB/JF
P27169
Serum paraoxonase/arylesterase 1
PON1
RB/JF
Q9Y233
cAMP and cAMP-inhibited cGMP 3′,5′-cyclic phosphodiesterase 10A
PDE10A
SR/ATT/JF
Q12791
Calcium-activated potassium channel subunit alpha 1
KCNMA1
SR/AFI
P35968
Vascular endothelial growth factor receptor 2
KDR
SR/RRER/JF
P54289
Voltage-dependent calcium channel subunit alpha-2/delta-1
CACNA2D1
SR
Q14432
CGMP-inhibited 3′,5′-cyclic phosphodiesterase A
PDE3A
SR/RPR/ATT/AFI/JF
P61925
cAMP-dependent protein kinase inhibitor alpha
PKIA
SR/RRER
P50613
Cell division protein kinase 7
CDK7
SR
P11712
Cytochrome P450 2C9
CYP2C9
SR
P99999
Cytochrome c
CYCS
SR/RPR/ATT
Q9GZT9
Egl nine homolog 1
EGLN1
SR/RPR/ATT
P00918
Carbonic anhydrase II
CA2
SR/AFI/ATT
P36544
Neuronal acetylcholine receptor protein, alpha-7 chain
CHRNA7
SR/JF
P49327
Fatty acid synthase
FASN
SR/AFI/RRER/JF
P21728
Dopamine D1 receptor
DRD1
SR/ATT/JF
P20309
Muscarinic acetylcholine receptor M3
CHRM3
SR/ATT/JF
P35348
Alpha-1A adrenergic receptor
ADRA1A
SR/JF
Q01959
Sodium-dependent dopamine transporter
SLC6A3
SR/JF
P31645
Sodium-dependent serotonin transporter
SLC6A4
SR/ATT/JF
P08263
Glutathione S-transferase A1
GSTA1
RPR
P09210
Glutathione S-transferase A2
GSTA2
RPR
P08588
Beta-1 adrenergic receptor
ADRB1
ATT
P08912
Muscarinic acetylcholine receptor M5
CHRM5
ATT/JF
P46098
5-Hydroxytryptamine receptor 3A
HTR3A
ATT/JF
P18825
Alpha-2C adrenergic receptor
ADRA2C
ATT/AFI/JF
P08173
Muscarinic acetylcholine receptor M4
CHRM4
ATT/JF
P41143
Delta-type opioid receptor
OPRD1
ATT/JF
P28223
5-Hydroxytryptamine 2A receptor
HTR2A
ATT/JF
P28335
5-Hydroxytryptamine 2C receptor
HTR2C
ATT/JF
P25100
Alpha-1D adrenergic receptor
ADRA1D
ATT/JF
P35372
Mu-type opioid receptor
OPRM1
ATT/JF
P28702
Retinoic acid receptor RXR-beta
RXRB
ATT
P00491
Purine nucleoside phosphorylase
PNP
ATT
P47712
Cytosolic phospholipase A2
PLA2G4A
AFI
Q09428
Sulfonylurea receptor 1
ABCC8
AFI
P56817
Beta-secretase
BACE1
AFI
P11802
Cell division protein kinase 4
CDK4
AFI
P05067
Amyloid beta A4 protein
APP
AFI
P55210
Caspase-7
CASP7
AFI
P08581
Hepatocyte growth factor receptor
MET
AFI
P27361
Mitogen-activated protein kinase 3
MAPK3
AFI
P01130
Low-density lipoprotein receptor
LDLR
AFI
P04040
Catalase
CAT
AFI/JF
P04035
3-Hydroxy-3-methylglutaryl-coenzyme A reductase
HMGCR
AFI
P00390
Glutathione reductase, mitochondrial
GSR
AFI
P33527
Multidrug resistance-associated protein 1
ABCC1
AFI
Q04828
Aldo-keto reductase family 1 member C1
AKR1C1
AFI
P17174
Aspartate aminotransferase, cytoplasmic
GOT1
AFI
P80404
4-Aminobutyrate aminotransferase, mitochondrial
ABAT
AFI
P35610
Sterol O-acyltransferase 1
SOAT1
AFI
Q13698
Voltage-dependent L-type calcium channel subunit alpha-1S
CACNA1S
JF
P27815
Type IV phosphodiesterase
PDE4A
JF
P08235
Mineralocorticoid receptor
NR3C2
JF
P21918
D(1B) dopamine receptor
DRD5
JF
P21917
D(4) dopamine receptor
DRD4
JF
P18089
Alpha-2B adrenergic receptor
ADRA2B
JF
Q15822
Neuronal acetylcholine receptor subunit alpha-2
CHRNA2
JF
P14416
D(2) dopamine receptor
DRD2
JF
P20813
Cytochrome P450 2B6
CYP2B6
JF
P08913
Alpha-2A adrenergic receptor
ADRA2A
JF
O75469
Pregnane X receptor
NR1I2
AFI


### 2.1.3. 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 gene-centric data from 125 web sources, including genomic, transcriptomic, proteomic, genetic, clinical, and functional information.


### 2.1.4. Protein-Protein Interaction Data

All the protein-protein 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. The 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].


## 2.1.1. Components of Each Herb in DCHD

Seventy-seven active components obtained from the eight herbs in DCHD were screened through a well-rounded literature search and the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP,http://lsp.nwu.edu.cn/tcmsp.php) [28]. As a bran-new TCM research platform and database based on systematic pharmacology, TCMSP contains comprehensive contents, including active components, key absorption, distribution, metabolism, excretion (ADME) properties, drug-likeness (DL), active targets, pathways involved, and related diseases of all herbs included in Chinese Pharmacopoeia. From the TCMSP database, we obtained a total of 349 compounds, including 143 components in Radix Bupleuri (RB), 138 in Scutellariae Radix (SR), 119 in Radix Paeoniae Rubra (RPR), 116 in Arum Ternatum Thunb (ATT), 65 in Aurantii Fructus Immaturus (AFI), 92 in Radix et Rhizoma Rhei (RERR), 265 in Zingiber Officinale Roscoe, (ZOR), and 134 in Jujubae Fructus (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 in Scutellariae Radix, 4 in Radix Paeoniae Rubra, 4 in Arum Ternatum Thunb, 16 in Aurantii Fructus Immaturus, 5 in Radix et Rhizoma Rhei, 1 in Zingiber Officinale Roscoe, and 14 in Jujubae Fructus (as shown in Figure 2).Table 1
72 potential compounds of Da-Chai-Hu Decoction and their network parameters.
Mol ID
Molecule name
Structure
MW
AlogP
Hdon
Hacc
OB (%)
DL
MOL013187
Cubebin
356.4
3.19
1
6
57.13
0.64
MOL004598
3,5,6,7-Tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone
432.46
2.54
0
9
31.97
0.59
MOL004624
Longikaurin A
348.48
1.16
3
5
47.72
0.53
MOL004609
Areapillin
360.34
2.29
3
8
48.96
0.41
MOL000354
Isorhamnetin
316.28
1.76
4
7
49.6
0.31
MOL000490
Petunidin
317.29
1.65
5
7
30.05
0.31
MOL000098
Quercetin
302.25
1.5
5
7
46.43
0.28
MOL000422
Kaempferol
286.25
1.77
4
6
41.88
0.24
MOL001458
Coptisine
320.34
3.25
0
4
30.67
0.86
MOL002897
Epiberberine
336.39
3.45
0
4
43.09
0.78
MOL002909
5,7,2,5-Tetrahydroxy-8,6-dimethoxyflavone
376.34
2.02
4
9
33.82
0.45
MOL002934
Neobaicalein
374.37
2.54
2
8
104.34
0.44
MOL002927
Skullcapflavone II
374.37
2.54
2
8
69.51
0.44
MOL012266
Rivularin
344.34
2.55
2
7
37.94
0.37
MOL000552
5,2′-Dihydroxy-6,7,8-trimethoxyflavone
344.34
2.55
2
7
31.71
0.35
MOL002915
Salvigenin
328.34
2.82
1
6
49.07
0.33
MOL002917
5,2′,6′-Trihydroxy-7,8-dimethoxyflavone
330.31
2.3
3
7
45.05
0.33
MOL002932
Panicolin
314.31
2.57
2
6
76.26
0.29
MOL012245
5,7,4′-Trihydroxy-6-methoxyflavanone
302.3
2.28
3
6
36.63
0.27
MOL002933
5,7,4′-Trihydroxy-8-methoxyflavone
300.28
2.32
3
6
36.56
0.27
MOL012246
5,7,4′-Trihydroxy-8-methoxyflavanone
302.3
2.28
3
6
74.24
0.26
MOL008206
Moslosooflavone
298.31
2.84
1
5
44.09
0.25
MOL002914
Eriodictyol (flavanone)
288.27
2.03
4
6
41.35
0.24
MOL002910
Carthamidin
288.27
2.03
4
6
41.15
0.24
MOL002925
5,7,2′,6′-Tetrahydroxyflavone
286.25
2.07
4
6
37.01
0.24
MOL001689
Acacetin
284.28
2.59
2
5
34.97
0.24
MOL002937
Dihydrooroxylin
286.3
2.55
2
5
66.06
0.23
MOL002928
Oroxylin A
284.28
2.59
2
5
41.37
0.23
MOL000173
Wogonin
284.28
2.59
2
5
30.68
0.23
MOL002913
Dihydrobaicalin_qt
272.27
2.3
3
5
40.04
0.21
MOL000525
Norwogonin
270.25
2.33
3
5
39.4
0.21
MOL002714
Baicalein
270.25
2.33
3
5
33.52
0.21
MOL000228
(2R)-7-Hydroxy-5-methoxy-2-phenylchroman-4-one
270.3
2.82
1
4
55.23
0.2
MOL001002
Ellagic acid
302.2
1.48
4
8
43.06
0.43
MOL001918
Paeoniflorgenone
318.35
0.79
1
6
87.59
0.37
MOL006992
(2R,3R)-4-Methoxyl-distylin
318.3
1.89
4
7
59.98
0.3
MOL002714
Baicalein
270.25
2.33
3
5
33.52
0.21
MOL002670
Cavidine
353.45
3.72
0
5
35.64
0.81
MOL006957
(3S,6S)-3-(Benzyl)-6-(4-hydroxybenzyl)piperazine-2,5-quinone
310.38
2.15
3
5
46.89
0.27
MOL006967
Beta-D-Ribofuranoside, xanthine-9
284.26
–1.29
5
9
44.72
0.21
MOL002714
Baicalein
270.25
2.33
3
5
33.52
0.21
MOL005828
Nobiletin
402.43
3.04
0
8
61.67
0.52
MOL001803
Sinensetin
372.4
3.06
0
7
50.56
0.45
MOL013277
Isosinensetin
372.4
3.06
0
7
51.15
0.44
MOL009053
4-[(2S,3R)-5-[(E)-3-Hydroxyprop-1-enyl]-7-methoxy-3-methylol-2,3-dihydrobenzofuran-2-yl]-2-methoxy-phenol
358.42
2.16
3
6
50.76
0.39
MOL007879
Tetramethoxyluteolin
342.37
3.07
0
6
43.68
0.37
MOL013435
Poncimarin
330.41
2.74
0
5
63.62
0.35
MOL013436
Isoponcimarin
330.41
2.94
0
5
63.28
0.31
MOL013279
5,7,4′-Trimethylapigenin
312.34
3.09
0
5
39.83
0.3
MOL013430
Prangenin
286.3
2.49
0
5
43.6
0.29
MOL001798
Neohesperidin_qt
302.3
2.28
3
6
71.17
0.27
MOL005100
5,7-Dihydroxy-2-(3-hydroxy-4-methoxyphenyl)chroman-4-one
302.3
2.28
3
6
47.74
0.27
MOL000006
Luteolin
286.25
2.07
4
6
36.16
0.25
MOL002914
Eriodictyol (flavanone)
288.27
2.03
4
6
41.35
0.24
MOL005849
Didymin
286.3
2.55
2
5
38.55
0.24
MOL001941
Ammidin
270.3
3.65
0
4
34.55
0.22
MOL004328
Naringenin
272.27
2.3
3
5
59.29
0.21
MOL002235
Eupatin
360.34
1.99
3
8
50.8
0.41
MOL002268
Rhein
284.23
1.88
3
6
47.07
0.28
MOL000471
Aloe-emodin
270.25
1.67
3
5
83.38
0.24
MOL000096
(−)-Catechin
290.29
1.92
5
6
49.68
0.24
MOL002281
Toralactone
272.27
2.25
2
5
46.46
0.24
MOL006129
6-Methylgingediacetate2
394.56
4.55
0
6
48.73
0.32
MOL000787
Fumarine
353.4
2.95
0
6
59.26
0.83
MOL001454
Berberine
336.39
3.45
0
4
36.86
0.78
MOL004350
Ruvoside_qt
390.57
2.29
3
5
36.12
0.76
MOL012986
Jujubasaponin V_qt
472.78
4.65
2
4
36.99
0.63
MOL012946
Ziziphus saponin I_qt
472.78
4.39
2
4
32.69
0.62
MOL000627
Stepholidine
327.41
3.1
2
5
33.11
0.54
MOL012992
Mauritine D
342.46
1.62
2
6
89.13
0.45
MOL007213
Nuciferine
295.41
3.57
0
3
34.43
0.4
MOL012976
Coumestrol
268.23
3.01
2
5
32.49
0.34
MOL012921
Stepharine
297.38
1.82
1
4
31.55
0.33
MOL001522
(S)-coclaurine
285.37
2.83
3
4
42.35
0.24
MOL000098
Quercetin
302.25
1.5
5
7
46.43
0.28
MOL000492
(+)-Catechin
290.29
1.92
5
6
54.83
0.24
MOL000096
(−)-Catechin
290.29
1.92
5
6
49.68
0.24Figure 2
Herb-compound network.(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. The components with MV from 180 to 500 Dalton are perceived as most available in drug therapy (MW ≤ 500).(2) AlogP Prediction. The 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. The 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. The 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 in-house system, OBioavail1.1 [32], and the compounds with OB ≥ 30% were filtered for further analysis. The 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. The DL of the compounds was calculated by the Tanimoto coefficient defined as follows:(1)TA,B=A⋅BA2+B2−A⋅B.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). The average of all descriptors was calculated by Dragon and the compounds with DL ≥ 0.18 were selected [35].From the above, in order to obtain the core active components, the screening principle was defined as follows: MW ≤ 500, AlogP ≤5, Hdon ≤5, Hacc ≤10, OB ≥ 30%, and DL ≥ 0.18.


## 2.1.2. 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 [27]. Eventually, 161 targets from DCHD were obtained without repeated targets of the same component (as shown in Table 2).Table 2
The Da-Chai-Hu Decoction-related target information.
UniProt ID
Target name
Gene name
Drug
P23219
Prostaglandin G/H synthase 1
PTGS1
RB/SR/RPR/ATT/RRER/AFI/JF
P00742
Coagulation factor Xa
F10
RB/SR/ATT/AFI/RRER/ZOR/JF
P35354
Prostaglandin G/H synthase 2
PTGS2
RB/SR/RPR/ATT/RRER/AFI/ZOR/JF
P07550
Beta-2 adrenergic receptor
ADRB2
RB/SR/ATT/AFI/JF
P08238
Heat-shock protein HSP 90
HSP90AB1
RB/SR/RPR/ATT/RRER/AFI/JF
P00734
Thrombin
F2
RB/SR/AFI/RRER/JF
Q99527
Estrogen receptor
GPER1
RB/SR/RPR/RRER/AFI/ZOR/JF
P10275
Androgen receptor
AR
RB/SR/RPR/ATT/AFI/RRER/JF
P08709
Coagulation factor VII
F7
RB/SR/ATT/AFI/RRER/JF
P22303
Acetylcholinesterase
ACHE
RB/SR/AFI/JF
P11388
DNA topoisomerase II
TOP2
RB/SR/ATT/AFI/RRER/JF
Q92731
Estrogen receptor-beta
ESTRB
RB/SR/AFI/RRER
P07477
Trypsin-1
PRSS1
RB/SR/RPR/ATT/RRER/AFI/JF
Q15596
Nuclear receptor coactivator 2
NCOA2
RB/SR/RPR/ATT/RRER/AFI/JF
P0DP23
Calmodulin
CALM
RB/SR/RPR/ATT/RRER/AFI/ZOR/JF
P11229
Muscarinic acetylcholine receptor M1
CHRM1
RB/SR/ATT/AFI/JF
P47869
Gamma-aminobutyric-acid receptor alpha-2 subunit
GABRA2
RB
P34903
Gamma-aminobutyric-acid receptor alpha-3 subunit
GABRA3
RB/JF
P08172
Muscarinic acetylcholine receptor M2
CHRM2
RB/JF
Q16445
Gamma-aminobutyric-acid receptor subunit alpha-6
GABRA6
RB
P35228
Nitric oxide synthase, inducible
NOS2
RB/SR/RPR/RRER/AFI/JF
Q14524
Sodium channel protein type 5 subunit alpha
SCN5A
RB/SR/ATT/RRER/AFI/JF
P18031
mRNA of protein-tyrosine phosphatase, nonreceptor type 1
PTPN1
RB/SR/AFI
P27487
Dipeptidyl peptidase IV
DPP4
RB/SR/RPR/ATT/RRER/AFI/JF
P01857
Ig gamma-1 chain C region
IGHG1
RB/SR/RRER
Q15788
Nuclear receptor coactivator 1
NCOA1
RB/SR/RPR/ATT/AFI/JF
P37231
Peroxisome proliferator-activated receptor-gamma
PPARG
RB/SR/AFI/JF
Q16539
Mitogen-activated protein kinase 14
MAPK14
RB/SR/AFI
P49841
Glycogen synthase kinase-3 beta
GSK3B
RB/SR/AFI
P24941
Cell division protein kinase 2
CDK2
RB/SR/AFI
P48736
Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit, gamma isoform
PIK3CG
RB/SR/RPR/ATT/RRER/AFI/JF
P17612
mRNA of PKA catalytic subunit C-alpha
PRKACA
RB/SR/RPR/ATT/RRER/AFI/JF
P11309
Proto-oncogene serine/threonine-protein kinase Pim-1
PIM1
RB/AFI
P20248
Cyclin-A2
CCNA2
RB
P11217
Glycogen phosphorylase, muscle form
PYGM
RB/SR/AFI
Q03181
Peroxisome proliferator-activated receptor delta
PPARD
RB/SR/RRER
O14757
Serine/threonine-protein kinase Chk1
CHEK1
RB/SR/AFI/RRER
P15121
Aldose reductase
AKR1B1
RB/RRER/JF
P29474
Nitric oxide synthase, endothelial
NOS3
RB/SR/AFI/JF
P14867
Gamma-aminobutyric acid receptor subunit alpha-1
GABRA1
RB/SR/RPR/AFI/JF
P27338
Amine oxidase (flavin-containing) B
MAOB
RB/SR/AFI/JF
P42262
Glutamate receptor 2
GRIA2
RB
P47989
Xanthine dehydrogenase/oxidase
XDH
RB/AFI/JF
P06401
Progesterone receptor
PGR
RB/RPR
P23975
Sodium-dependent noradrenaline transporter
SLC6A2
RB/JF
P35368
Alpha-1B adrenergic receptor
ADRA1B
RB/SR/ATT/AFI/JF
P10415
Apoptosis regulator Bcl-2
BCL2
RB/SR/RPR/ATT/AFI/JF
P01375
Tumor necrosis factor
TNF
RB/SR/AFI/RRER/JF
P05412
Transcription factor AP-1
JUN
RB/SR/AFI/RRER/JF
P45983
Mitogen-activated protein kinase 8
MAPK8
RB/AFI
P03956
Interstitial collagenase
MMP1
RB/SR/AFI/JF
P06493
Cell division control protein 2 homolog
CDC2
RB/SR/RPR/ATT/RRER/JF
P09601
Heme oxygenase 1
HMOX1
RB/AFI/JF
P08684
Cytochrome P450 3A4
CYP3A4
RB/JF
P05177
Cytochrome P450 1A2
CYP1A2
RB/SR/JF
P16581
E-selectin
SELE
RB/JF
P19320
Vascular cell adhesion protein 1
VCAM1
RB/JF
P09917
Arachidonate 5-lipoxygenase
ALOX5
RB/JF
P09211
Glutathione S-transferase P
GSTP1
RB/RPR/AFI/JF
P35869
Aryl hydrocarbon receptor
AHR
RB/SR/RPR/ATT/JF
P06213
Insulin receptor
INSR
RB/AFI/JF
Q08209
Serine/threonine-protein phosphatase 2B catalytic subunit alpha isoform
PPP3CA
RB
P09488
Glutathione S-transferase Mu 1
GSTM1
RB/RPR/JF
P28161
Glutathione S-transferase Mu 2
GSTM2
RB/RPR/JF
P42330
Aldo-keto reductase family 1 member C3
AKR1C3
RB
Q12809
Potassium voltage-gated channel subfamily H member 2
KCNH2
RB/SR/ATT/AFI/JF
P08254
Stromelysin-1
MMP3
RB/JF
P19793
Retinoic acid receptor RXR-alpha
RXRA
RB/SR/ATT/AFI/JF
P00533
Epidermal growth factor receptor
EGFR
RB/AFI/JF
P15692
Vascular endothelial growth factor A
VEGFA
RB/SR/ATT/RRER/AFI/JF
P00749
Urokinase-type plasminogen activator
PLAU
RB/JF
P08253
72 kDa type IV collagenase
MMP2
RB/RPR/AFI/JF
P28482
Mitogen-activated protein kinase 1
MAPK1
RB/SR/AFI/JF
P01133
Proepidermal growth factor
EGF
RB/JF
P06400
Retinoblastoma-associated protein
RB1
RB/AFI/JF
P05231
Interleukin-6
IL6
RB/SR/AFI/JF
P04637
Cellular tumor antigen p53
TP53
RB/SR/RPR/ATT/RRER/AFI/JF
P16435
NADPH-cytochrome P450 reductase
POR
RB/JF
P11926
Ornithine decarboxylase
ODC1
RB/JF
P11387
DNA topoisomerase 1
TOP1
RB/AFI/JF
P00441
Superoxide dismutase (Cu-Zn)
SOD1
RB/AFI/JF
P11021
78 kDa glucose-regulated protein
HSPA5
RB/JF
Q13085
Acetyl-CoA carboxylase 1
ACACA
RB/JF
P13726
Tissue factor
F3
RB/JF
P17302
Gap junction alpha-1 protein
GJA1
RB/JF
P01584
Interleukin-1 beta
IL1B
RB/RRER/JF
Q99616
C-C motif chemokine 2
CCL13
RB/SR/JF
P43115
Prostaglandin E2 receptor EP3 subtype
PTGER3
RB/SR/JF
P49888
Estrogen sulfotransferase
SULT1E1
RB/SR/JF
O43451
Maltase-glucoamylase, intestinal
MGAM
RB/SR/JF
P60568
Interleukin-2
IL2
RB/AFI/JF
P00750
Tissue-type plasminogen activator
PLAT
RB/JF
P07204
Thrombomodulin
THBD
RB/JF
P02452
Collagen alpha-1(I) chain
COL1A1
RB/JF
P01579
Interferon-gamma
IFNG
RB/AFI/JF
P05164
Myeloperoxidase
MPO
RB/SR/RPR/ATT/JF
P15559
NAD(P)H dehydrogenase [quinone] 1
NQO1
RB/JF
P02461
Collagen alpha-1(III) chain
COL3A1
RB/JF
P15309
Prostatic acid phosphatase
ACPP
RB/JF
P07339
Cathepsin D
CTSD
RB/JF
P27169
Serum paraoxonase/arylesterase 1
PON1
RB/JF
Q9Y233
cAMP and cAMP-inhibited cGMP 3′,5′-cyclic phosphodiesterase 10A
PDE10A
SR/ATT/JF
Q12791
Calcium-activated potassium channel subunit alpha 1
KCNMA1
SR/AFI
P35968
Vascular endothelial growth factor receptor 2
KDR
SR/RRER/JF
P54289
Voltage-dependent calcium channel subunit alpha-2/delta-1
CACNA2D1
SR
Q14432
CGMP-inhibited 3′,5′-cyclic phosphodiesterase A
PDE3A
SR/RPR/ATT/AFI/JF
P61925
cAMP-dependent protein kinase inhibitor alpha
PKIA
SR/RRER
P50613
Cell division protein kinase 7
CDK7
SR
P11712
Cytochrome P450 2C9
CYP2C9
SR
P99999
Cytochrome c
CYCS
SR/RPR/ATT
Q9GZT9
Egl nine homolog 1
EGLN1
SR/RPR/ATT
P00918
Carbonic anhydrase II
CA2
SR/AFI/ATT
P36544
Neuronal acetylcholine receptor protein, alpha-7 chain
CHRNA7
SR/JF
P49327
Fatty acid synthase
FASN
SR/AFI/RRER/JF
P21728
Dopamine D1 receptor
DRD1
SR/ATT/JF
P20309
Muscarinic acetylcholine receptor M3
CHRM3
SR/ATT/JF
P35348
Alpha-1A adrenergic receptor
ADRA1A
SR/JF
Q01959
Sodium-dependent dopamine transporter
SLC6A3
SR/JF
P31645
Sodium-dependent serotonin transporter
SLC6A4
SR/ATT/JF
P08263
Glutathione S-transferase A1
GSTA1
RPR
P09210
Glutathione S-transferase A2
GSTA2
RPR
P08588
Beta-1 adrenergic receptor
ADRB1
ATT
P08912
Muscarinic acetylcholine receptor M5
CHRM5
ATT/JF
P46098
5-Hydroxytryptamine receptor 3A
HTR3A
ATT/JF
P18825
Alpha-2C adrenergic receptor
ADRA2C
ATT/AFI/JF
P08173
Muscarinic acetylcholine receptor M4
CHRM4
ATT/JF
P41143
Delta-type opioid receptor
OPRD1
ATT/JF
P28223
5-Hydroxytryptamine 2A receptor
HTR2A
ATT/JF
P28335
5-Hydroxytryptamine 2C receptor
HTR2C
ATT/JF
P25100
Alpha-1D adrenergic receptor
ADRA1D
ATT/JF
P35372
Mu-type opioid receptor
OPRM1
ATT/JF
P28702
Retinoic acid receptor RXR-beta
RXRB
ATT
P00491
Purine nucleoside phosphorylase
PNP
ATT
P47712
Cytosolic phospholipase A2
PLA2G4A
AFI
Q09428
Sulfonylurea receptor 1
ABCC8
AFI
P56817
Beta-secretase
BACE1
AFI
P11802
Cell division protein kinase 4
CDK4
AFI
P05067
Amyloid beta A4 protein
APP
AFI
P55210
Caspase-7
CASP7
AFI
P08581
Hepatocyte growth factor receptor
MET
AFI
P27361
Mitogen-activated protein kinase 3
MAPK3
AFI
P01130
Low-density lipoprotein receptor
LDLR
AFI
P04040
Catalase
CAT
AFI/JF
P04035
3-Hydroxy-3-methylglutaryl-coenzyme A reductase
HMGCR
AFI
P00390
Glutathione reductase, mitochondrial
GSR
AFI
P33527
Multidrug resistance-associated protein 1
ABCC1
AFI
Q04828
Aldo-keto reductase family 1 member C1
AKR1C1
AFI
P17174
Aspartate aminotransferase, cytoplasmic
GOT1
AFI
P80404
4-Aminobutyrate aminotransferase, mitochondrial
ABAT
AFI
P35610
Sterol O-acyltransferase 1
SOAT1
AFI
Q13698
Voltage-dependent L-type calcium channel subunit alpha-1S
CACNA1S
JF
P27815
Type IV phosphodiesterase
PDE4A
JF
P08235
Mineralocorticoid receptor
NR3C2
JF
P21918
D(1B) dopamine receptor
DRD5
JF
P21917
D(4) dopamine receptor
DRD4
JF
P18089
Alpha-2B adrenergic receptor
ADRA2B
JF
Q15822
Neuronal acetylcholine receptor subunit alpha-2
CHRNA2
JF
P14416
D(2) dopamine receptor
DRD2
JF
P20813
Cytochrome P450 2B6
CYP2B6
JF
P08913
Alpha-2A adrenergic receptor
ADRA2A
JF
O75469
Pregnane X receptor
NR1I2
AFI


## 2.1.3. 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 gene-centric data from 125 web sources, including genomic, transcriptomic, proteomic, genetic, clinical, and functional information.


## 2.1.4. Protein-Protein Interaction Data

All the protein-protein 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. The 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].


## 2.2. Network Construction

### 2.2.1. Network Construction

The 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.


### 2.2.2. Network Topological Features

There 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]. The levels of the aforementioned three parameters reflect the importance of a node in the network. The higher the value of the parameter is, the more important the node becomes.


### 2.2.3. Gene Ontology (GO) Enrichment and Pathway Analysis

The 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.


## 2.2.1. Network Construction

The 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.


## 2.2.2. Network Topological Features

There 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]. The levels of the aforementioned three parameters reflect the importance of a node in the network. The higher the value of the parameter is, the more important the node becomes.


## 2.2.3. Gene Ontology (GO) Enrichment and Pathway Analysis

The 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.


## 3. Results and Discussion

### 3.1. Compound-Compound Target Network Analysis

This network contained 238 nodes (161 compound target nodes and 77 active compound nodes), as shown in Figure3. On the one hand, there were many compound targets corresponding to multiple components in the network, suggesting that different components often have common targets. The 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. Thus, from the network, we can have a general observation on the relationships between active compounds and targets from the compound-compound target network. The targets of all herbal composition in DCHD correspond to a variety of diseases besides T2DM, which can exert pharmacological effects on multiple diseases. Therefore, this network also fully reflects the characteristics of multicomponent-multitarget-multidisease of TCM formula.Figure 3
Target-compound network.


### 3.2. T2DM Network Analysis

#### 3.2.1. T2DM PPI Network

The 161 targets of DCHD were corresponding to 200 targets of T2DM, and 38 repeat important targets were obtained (as shown in Table3). 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. This network was composed of 38 nodes and 349 edges. The more the edges between two nodes are, the greater the degree of association between the two targets exhibits. Then, 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). The 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. The 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]. The 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. Therefore, TP53-mediated gene expression plays a vital role in the development of metabolic disorders in patients with T2DM [48, 49].Table 3
The related targets' information of D-D.
Number
Gene name
1
PTGS2
2
ACHE
3
NOS2
4
PPARG
5
GSK3B
6
NOS3
7
BCL2
8
TNF
9
JUN
10
HMOX1
11
CYP1A2
12
VCAM1
13
AHR
14
INSR
15
GSTM1
16
MAPK1
17
EGF
18
IL6
19
TP53
20
POR
21
SOD1
22
GJA1
23
IL1B
24
COL1A1
25
IFNG
26
MPO
27
NQO1
28
PON1
29
KDR
30
CYCS
31
GSTA1
32
APP
33
MAPK3
34
LDLR
35
CAT
36
HMGCR
37
GSR
38
XDHFigure 4
The process of topological screening for the PPI network. The yellow nodes represent the core targets, and the blue nodes represent the noncore targets.


#### 3.2.2. Functional Analysis and Pathways of T2DM

In order to further clarify the biological functions of target genesin 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 Figure5, 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.Figure 5
GO enrichment analysis of 30 nodes in biological process. “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).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. Therefore, 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. Therefore, inhibition of p38MAPK enzyme activity could remarkably reduce the expression of inflammatory factors [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. The 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]. Therefore, 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 insulin-sensitizing 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.The 38 core targets were imported into the DAVID, and 89 pathways were obtained. Through systematic review of relevant literature and summary of T2DM-related pathogenesis, 20 signal pathways closely related to T2DM were screened. To further integrate T2DM core targets, we finally detected two core pathways (Figure6), 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-γ (PPAR-γ), and B-cell lymphoma-2 (Bcl-2) get involved in the regulation of apoptosis through PI3K/AKT signaling pathway. Therefore, the therapeutic mechanism of DCHD on T2DM mainly includes the following two modules: inflammation and cell apoptosis.Figure 6
Target-pathway network. The red triangles represent the related pathways, and the blue round nodes represent the hub nodes.(1) Inflammation Module. T2DM is a low-grade inflammatory response disease [61]. The 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 anti-inflammatory “M2” (alternate activation) to a proinflammatory “M1” (classical activation) phenotype, inducing the increase of inflammatory factors [63]. Therefore, T2DM contains a large number of inflammatory factors triggering cascade reactions of inflammatory signals. This 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. This results in the dissociation of NF-κB from its inhibitor IĸBα, exposing the nuclear localization sequence of NF-κB. And then, NF-κB is transferred to the nucleus to promote NF-κB-dependent gene transcription. NF-κB mainly regulates the transcriptional effects of IL-6, IL-1β, VCAM-1, COX-2, TNF-α, and JUN [64–67], thereby mediating the expression of proinflammatory factors to enhance inflammation.(2) Apoptosis Module. The fundamental pathophysiological mechanism of T2DM is insulin resistance, which results in islet β-cell dysfunction and, in extreme cases, apoptosis of β-cells. Therefore, 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. Therefore, the occurrence and development 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. The 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. The 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. The 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-γ. 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. There 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 [79–81]. Therefore, 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-γ is an important cell differentiation transcription factor, mainly expressed in adipose and skeletal muscle tissues. Activation of PPAR-γ 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-γ 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-γ, and inhibition of other pro-β-cell apoptosis targets such as TNF can inhibit β-cell apoptosis, thereby effectively controlling the development of T2DM.Figure 7
The T2DM pathway and therapeutic modules. The red rectangles represent the core targets, and the green rectangles represent the noncore targets.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. Therefore, further experimental validation to increase the accuracy and feasibility of the research results is warranted.


## 3.1. Compound-Compound Target Network Analysis

This network contained 238 nodes (161 compound target nodes and 77 active compound nodes), as shown in Figure3. On the one hand, there were many compound targets corresponding to multiple components in the network, suggesting that different components often have common targets. The 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. Thus, from the network, we can have a general observation on the relationships between active compounds and targets from the compound-compound target network. The targets of all herbal composition in DCHD correspond to a variety of diseases besides T2DM, which can exert pharmacological effects on multiple diseases. Therefore, this network also fully reflects the characteristics of multicomponent-multitarget-multidisease of TCM formula.Figure 3
Target-compound network.


## 3.2. T2DM Network Analysis

### 3.2.1. T2DM PPI Network

The 161 targets of DCHD were corresponding to 200 targets of T2DM, and 38 repeat important targets were obtained (as shown in Table3). 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. This network was composed of 38 nodes and 349 edges. The more the edges between two nodes are, the greater the degree of association between the two targets exhibits. Then, 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). The 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. The 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]. The 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. Therefore, TP53-mediated gene expression plays a vital role in the development of metabolic disorders in patients with T2DM [48, 49].Table 3
The related targets' information of D-D.
Number
Gene name
1
PTGS2
2
ACHE
3
NOS2
4
PPARG
5
GSK3B
6
NOS3
7
BCL2
8
TNF
9
JUN
10
HMOX1
11
CYP1A2
12
VCAM1
13
AHR
14
INSR
15
GSTM1
16
MAPK1
17
EGF
18
IL6
19
TP53
20
POR
21
SOD1
22
GJA1
23
IL1B
24
COL1A1
25
IFNG
26
MPO
27
NQO1
28
PON1
29
KDR
30
CYCS
31
GSTA1
32
APP
33
MAPK3
34
LDLR
35
CAT
36
HMGCR
37
GSR
38
XDHFigure 4
The process of topological screening for the PPI network. The yellow nodes represent the core targets, and the blue nodes represent the noncore targets.


### 3.2.2. Functional Analysis and Pathways of T2DM

In order to further clarify the biological functions of target genesin 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 Figure5, 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.Figure 5
GO enrichment analysis of 30 nodes in biological process. “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).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. Therefore, 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. Therefore, inhibition of p38MAPK enzyme activity could remarkably reduce the expression of inflammatory factors [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. The 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]. Therefore, 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 insulin-sensitizing 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.The 38 core targets were imported into the DAVID, and 89 pathways were obtained. Through systematic review of relevant literature and summary of T2DM-related pathogenesis, 20 signal pathways closely related to T2DM were screened. To further integrate T2DM core targets, we finally detected two core pathways (Figure6), 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-γ (PPAR-γ), and B-cell lymphoma-2 (Bcl-2) get involved in the regulation of apoptosis through PI3K/AKT signaling pathway. Therefore, the therapeutic mechanism of DCHD on T2DM mainly includes the following two modules: inflammation and cell apoptosis.Figure 6
Target-pathway network. The red triangles represent the related pathways, and the blue round nodes represent the hub nodes.(1) Inflammation Module. T2DM is a low-grade inflammatory response disease [61]. The 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 anti-inflammatory “M2” (alternate activation) to a proinflammatory “M1” (classical activation) phenotype, inducing the increase of inflammatory factors [63]. Therefore, T2DM contains a large number of inflammatory factors triggering cascade reactions of inflammatory signals. This 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. This results in the dissociation of NF-κB from its inhibitor IĸBα, exposing the nuclear localization sequence of NF-κB. And then, NF-κB is transferred to the nucleus to promote NF-κB-dependent gene transcription. NF-κB mainly regulates the transcriptional effects of IL-6, IL-1β, VCAM-1, COX-2, TNF-α, and JUN [64–67], thereby mediating the expression of proinflammatory factors to enhance inflammation.(2) Apoptosis Module. The fundamental pathophysiological mechanism of T2DM is insulin resistance, which results in islet β-cell dysfunction and, in extreme cases, apoptosis of β-cells. Therefore, 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. Therefore, the occurrence and development 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. The 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. The 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. The 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-γ. 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. There 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 [79–81]. Therefore, 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-γ is an important cell differentiation transcription factor, mainly expressed in adipose and skeletal muscle tissues. Activation of PPAR-γ 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-γ 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-γ, and inhibition of other pro-β-cell apoptosis targets such as TNF can inhibit β-cell apoptosis, thereby effectively controlling the development of T2DM.Figure 7
The T2DM pathway and therapeutic modules. The red rectangles represent the core targets, and the green rectangles represent the noncore targets.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. Therefore, further experimental validation to increase the accuracy and feasibility of the research results is warranted.


## 3.2.1. T2DM PPI Network

The 161 targets of DCHD were corresponding to 200 targets of T2DM, and 38 repeat important targets were obtained (as shown in Table3). 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. This network was composed of 38 nodes and 349 edges. The more the edges between two nodes are, the greater the degree of association between the two targets exhibits. Then, 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). The 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. The 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]. The 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. Therefore, TP53-mediated gene expression plays a vital role in the development of metabolic disorders in patients with T2DM [48, 49].Table 3
The related targets' information of D-D.
Number
Gene name
1
PTGS2
2
ACHE
3
NOS2
4
PPARG
5
GSK3B
6
NOS3
7
BCL2
8
TNF
9
JUN
10
HMOX1
11
CYP1A2
12
VCAM1
13
AHR
14
INSR
15
GSTM1
16
MAPK1
17
EGF
18
IL6
19
TP53
20
POR
21
SOD1
22
GJA1
23
IL1B
24
COL1A1
25
IFNG
26
MPO
27
NQO1
28
PON1
29
KDR
30
CYCS
31
GSTA1
32
APP
33
MAPK3
34
LDLR
35
CAT
36
HMGCR
37
GSR
38
XDHFigure 4
The process of topological screening for the PPI network. The yellow nodes represent the core targets, and the blue nodes represent the noncore targets.


## 3.2.2. Functional Analysis and Pathways of T2DM

In order to further clarify the biological functions of target genesin 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 Figure5, 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.Figure 5
GO enrichment analysis of 30 nodes in biological process. “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).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. Therefore, 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. Therefore, inhibition of p38MAPK enzyme activity could remarkably reduce the expression of inflammatory factors [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. The 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]. Therefore, 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 insulin-sensitizing 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.The 38 core targets were imported into the DAVID, and 89 pathways were obtained. Through systematic review of relevant literature and summary of T2DM-related pathogenesis, 20 signal pathways closely related to T2DM were screened. To further integrate T2DM core targets, we finally detected two core pathways (Figure6), 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-γ (PPAR-γ), and B-cell lymphoma-2 (Bcl-2) get involved in the regulation of apoptosis through PI3K/AKT signaling pathway. Therefore, the therapeutic mechanism of DCHD on T2DM mainly includes the following two modules: inflammation and cell apoptosis.Figure 6
Target-pathway network. The red triangles represent the related pathways, and the blue round nodes represent the hub nodes.(1) Inflammation Module. T2DM is a low-grade inflammatory response disease [61]. The 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 anti-inflammatory “M2” (alternate activation) to a proinflammatory “M1” (classical activation) phenotype, inducing the increase of inflammatory factors [63]. Therefore, T2DM contains a large number of inflammatory factors triggering cascade reactions of inflammatory signals. This 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. This results in the dissociation of NF-κB from its inhibitor IĸBα, exposing the nuclear localization sequence of NF-κB. And then, NF-κB is transferred to the nucleus to promote NF-κB-dependent gene transcription. NF-κB mainly regulates the transcriptional effects of IL-6, IL-1β, VCAM-1, COX-2, TNF-α, and JUN [64–67], thereby mediating the expression of proinflammatory factors to enhance inflammation.(2) Apoptosis Module. The fundamental pathophysiological mechanism of T2DM is insulin resistance, which results in islet β-cell dysfunction and, in extreme cases, apoptosis of β-cells. Therefore, 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. Therefore, the occurrence and development 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. The 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. The 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. The 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-γ. 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. There 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 [79–81]. Therefore, 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-γ is an important cell differentiation transcription factor, mainly expressed in adipose and skeletal muscle tissues. Activation of PPAR-γ 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-γ 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-γ, and inhibition of other pro-β-cell apoptosis targets such as TNF can inhibit β-cell apoptosis, thereby effectively controlling the development of T2DM.Figure 7
The T2DM pathway and therapeutic modules. The red rectangles represent the core targets, and the green rectangles represent the noncore targets.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. Therefore, further experimental validation to increase the accuracy and feasibility of the research results is warranted.


## 4. 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. The 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.



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*Source: 9768414-2020-04-28.xml*