# GEPSI: A Gene Expression Profile Similarity-Based Identification Method of Bioactive Components in Traditional Chinese Medicine Formula

Authors: Baixia Zhang; Shuaibing He; Chenyang Lv; Yanling Zhang; Yun Wang
Journal: Evidence-Based Complementary and Alternative Medicine
 (2018)

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## Abstract

The identification of bioactive components in traditional Chinese medicine (TCM) is an important part of the TCM material foundation research. Recently, molecular docking technology has been extensively used for the identification of TCM bioactive components. However, target proteins that are used in molecular docking may not be the actual TCM target. For this reason, the bioactive components would likely be omitted or incorrect. To address this problem, this study proposed the GEPSI method that identified the target proteins of TCM based on the similarity of gene expression profiles. The similarity of the gene expression profiles affected by TCM and small molecular drugs was calculated. The pharmacological action of TCM may be similar to that of small molecule drugs that have a high similarity score. Indeed, the target proteins of the small molecule drugs could be considered TCM targets. Thus, we identified the bioactive components of a TCM by molecular docking and verified the reliability of this method by a literature investigation. Using the target proteins that TCM actually affected as targets, the identification of the bioactive components was more accurate. This study provides a fast and effective method for the identification of TCM bioactive components.


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## Body

## 1. Introduction

A method to identify the bioactive components in traditional Chinese medicine (TCM) from their complex mixtures is a critical challenge of TCM research. Because of its intuitive and efficient characteristics, molecular docking has become an important means for the identification of TCM bioactive components. The basis of identification via molecular docking involves one or multiple target proteins and the components being screened; ultimately, the components that specifically act on target protein can be identified, such as TCM bioactive components. In the screening process, a single target or multiple targets are chosen, usually targets associated with a specific disease. Methods for choosing targets are generally based on a database of disease-associated targets, a key target in a signaling transduction network or from the literature [1–3]. Because of the complexity of a disease, multiple targets may be associated with it. Therefore, the target proteins selected may not be the actual targets affected by TCM, or it may not be possible to screen against all of the associated targets. Therefore, the bioactive components obtained by molecular docking may not be the components that actually cured the corresponding disease or have been left out.The development of chemical informatics and bioinformatics has led to the accumulation of data on TCM components, target proteins, and gene expression profiles. To determine a method for the selection of target proteins for molecular docking guided by the ideas of system pharmacology, this study proposed a method for determining the target proteins of TCM and then identified the bioactive components of TCM by molecular docking. This method has been designated the gene expression profile similarity-based identification (GEPSI) method. The basic concept is to choose the gene expression profiles that are targeted by small molecule drugs in Cmap based on the principle that they have higher comparability with the gene expression profiles of a TCM, and calculate the gene expression profiles similarity between the TCM and the small molecule drugs. The target proteins of the small molecule drugs that have higher similarity scores could be considered TCM targets. Aiming at these target proteins, virtual screening is carried out to screen the TCM components, ultimately identifying the bioactive components. Because it considers the entirety of the TCM components and all of the genes affected as the object, this method could embody the holistic thinking of TCM research more concretely. This method provides an effective means for the identification of TCM bioactive components and could serve as a basis for drug repositioning, quality control, and TCM drug design.


## 2. Methods and Materials

### 2.1. Principle of GEPSI

Both TCMs and small molecule drugs all can affect gene expression. By comparing the gene expression profiles before and after treatment with TCM or a small molecule drug, up- and downregulated differentially expressed genes can be identified. Then, these up and downregulated differentially expressed genes that are affected by TCMs and small molecule drugs can be compared, and a similarity score can be obtained. If the similarity score is high, the TCM and the small molecule drug may have similar pharmacological action, and the target proteins for the small molecule drugs that have higher similarity score can be considered targets for the TCM. Using these proteins as the targets, we can finally identify the bioactive components of a TCM by molecular docking. We also discuss each step of the ITPI method (Figure1) in detail in this paper.Figure 1
The workflow of GEPSI.


#### 2.1.1. Gene Expression Profile Data

The Gene Expression Omnibus (GEO,https://www.ncbi.nlm.nih.gov/geo/) contains some gene expression profiles which were treated with components, herbs, and TCM formulae. These data were utilized to carry out TCM-related research. Connectivity Map (CMap, https://www.broadinstitute.org/cmap/) is a gene expression profile database related to small molecule drugs [34]. Cmap establishes the relations of small molecule drugs, genes, and diseases according to the gene expression differences in human tumor cell lines after treatment with small molecule drugs. By comparing the similarity of different gene expression profiles, Cmap is mainly applicable to the areas of drug development, such as drug repositioning. Different human tumor cell lines (HL60, MCF7, PC3, SKMEL5, and ssMCF7) were treated with small molecules drugs at different concentrations (10 nM, 100 nM, 1 μM, and 10 μM) for different times (6 h, 12 h). At present, Cmap contains data for 1309 small molecule drugs and more than 7000 gene expression profiles. Of these 1309 drugs, 556 drugs were recorded in DrugBank. Of these 556 drugs, 522 drugs had the data of target protein. This study chose gene expression profiles that had high similarity to TCMs with respect to the same cell lines types and platforms.


#### 2.1.2. The Determination of Up and Downregulated Genes

The differentially expressed genes were determined using the bioinformatics toolbox of Matlab [35]. At-test and false discovery rate (FDR) of multiple hypothesis testing were performed on each gene. Significant differentially expressed genes were detected by random sample replacement (P<0.05, FDR ≤ 0.1). Up and downregulated genes were distinguished by the magnitude of fold change (FC). If FC ≥ 2, then the significant differentially expressed genes were up-regulated genes, and if FC ≤ 0.5, then the significant differentially expressed genes were downregulated genes.


#### 2.1.3. The Similarity Computation of the Gene Expression Profile

The up- and downregulated genes were used to calculate the gene expression profile similarity. Using up and downregulated genes as the base data, the gene expression profile similarity was automatically calculated in Cmap by the K-S algorithm [34, 36]. A similarity comparison yielded the similarity scores of the gene expression profiles of each small molecule drugs and TCM. Similarity scores fell between −1 and 1. If 0 ≤ similarity scores ≤ 1, the pharmacological action of a small molecule drug and TCM were similar, and a higher absolute value of the similarity score indicated a greater similarity; if −1 ≤ similarity scores ≤ 0, the pharmacological action of a small molecule drug and TCM were adverse, and a higher absolute value indicated less similarity.


#### 2.1.4. Determination of the TCM Target Proteins

If the similarity score for the gene expression profiles of a small molecule drug and TCM was high, then their pharmacological action was similar. The target proteins of a small molecule drugs were considered TCM targets. This study only considered the top 10 small molecule drugs that had a definite pharmacological action and their target proteins were recorded in DrugBank version 4.3.


#### 2.1.5. Data for the TCM Components

The components of a TCM formula were collected from TCMD [37] and TCMSP [38]. The components were supplemented and perfected by the literature in CNKI and PubMed (1979~2017). The name, structure, and SMILES string of a component was recorded. For components with synonyms, the repetitive components were deleted by the “full structure” algorithm in “ChemBioFinder for Office 12.0”.


#### 2.1.6. Determination of Bioactive Components of a TCM

The three-dimensional structure was downloaded from the PDB (https://www.rcsb.org/pdb/home/home.do), and the structure that had active ligands and higher resolution was preferentially selected. The preprocessing of the target protein included the deletion of ligands, water, and redundant protein conformations; the completion of missing or incomplete residues; the addition of hydrogens; and the distribution of related charges. The amino acids in the target protein that interact with the ligand were selected and were defined as the active pocket. The structure of components was transformed into a three-dimensional structure, endowed with a CHARMM force field and protonated in accordance with the corresponding pH. Molecular docking was carried out by LibDock [39], and the parameter settings were as follows: the “Conformation Method” was “BEST,” the “Docking Preferences” was “High Quality,” and the other parameters were set to the default. With the “LibDock Score” as the reference, the components that had a score higher than the ligand and the ranked in the top 10 were considered the bioactive components. This information allowed us to identify the bioactive components of the TCM.


### 2.2. The Application of GEPSI on SWT

Si-Wu-Tang (SWT) is a well-known TCM formula and is prepared from four medicinal herbs includingRehmanniae Radix Praeparata (Rehmannia glutinosa Libosch.),Angelicae Sinensis Radix (Angelica sinensis (Oliv.) Diels),Paeoniae Radix Alba (Paeonia lactiflora Pall.), andChuanxiong Rhizoma (Ligusticum chuanxiong Hort.).SWT and its series of decoctions (i.e., the Xiang-Fu-Si-Wu decoction, and the Tao-Hong-Si-Wu decoction) have been widely used in clinical gynecology practice for blood stasis syndrome, such as primary dysmenorrheal, breast cancer, and other estrogen-related diseases [40–44]. ForSWT, this study applied the TCM bioactive components identification method based on the similarity of the gene expression profiles. In GEO, the number of gene expression profiles thatSWT (0.0256 mg/mL, 0.256 mg/mL, and 2.56 mg/mL) acted on, MCF-7, was GSE23610. GSE23610 was obtained on the GPL570 platform (HG-U133_Plus_2) [45]. A total of 3905 gene expression profiles were selected in Cmap for the same cell line (MCF-7) and platform (HG-U133_Plus_2). These gene expression profiles involved 1294 small molecule drugs. In addition, 98 components ofRehmanniae Radix Praeparata, 215 components ofAngelicae Sinensis Radix, 85 components ofPaeoniae Radix Alba, and 258 components ofChuanxiong Rhizoma were collected. The collected components can be seen in the Supplemental Information 1.


## 2.1. Principle of GEPSI

Both TCMs and small molecule drugs all can affect gene expression. By comparing the gene expression profiles before and after treatment with TCM or a small molecule drug, up- and downregulated differentially expressed genes can be identified. Then, these up and downregulated differentially expressed genes that are affected by TCMs and small molecule drugs can be compared, and a similarity score can be obtained. If the similarity score is high, the TCM and the small molecule drug may have similar pharmacological action, and the target proteins for the small molecule drugs that have higher similarity score can be considered targets for the TCM. Using these proteins as the targets, we can finally identify the bioactive components of a TCM by molecular docking. We also discuss each step of the ITPI method (Figure1) in detail in this paper.Figure 1
The workflow of GEPSI.


### 2.1.1. Gene Expression Profile Data

The Gene Expression Omnibus (GEO,https://www.ncbi.nlm.nih.gov/geo/) contains some gene expression profiles which were treated with components, herbs, and TCM formulae. These data were utilized to carry out TCM-related research. Connectivity Map (CMap, https://www.broadinstitute.org/cmap/) is a gene expression profile database related to small molecule drugs [34]. Cmap establishes the relations of small molecule drugs, genes, and diseases according to the gene expression differences in human tumor cell lines after treatment with small molecule drugs. By comparing the similarity of different gene expression profiles, Cmap is mainly applicable to the areas of drug development, such as drug repositioning. Different human tumor cell lines (HL60, MCF7, PC3, SKMEL5, and ssMCF7) were treated with small molecules drugs at different concentrations (10 nM, 100 nM, 1 μM, and 10 μM) for different times (6 h, 12 h). At present, Cmap contains data for 1309 small molecule drugs and more than 7000 gene expression profiles. Of these 1309 drugs, 556 drugs were recorded in DrugBank. Of these 556 drugs, 522 drugs had the data of target protein. This study chose gene expression profiles that had high similarity to TCMs with respect to the same cell lines types and platforms.


### 2.1.2. The Determination of Up and Downregulated Genes

The differentially expressed genes were determined using the bioinformatics toolbox of Matlab [35]. At-test and false discovery rate (FDR) of multiple hypothesis testing were performed on each gene. Significant differentially expressed genes were detected by random sample replacement (P<0.05, FDR ≤ 0.1). Up and downregulated genes were distinguished by the magnitude of fold change (FC). If FC ≥ 2, then the significant differentially expressed genes were up-regulated genes, and if FC ≤ 0.5, then the significant differentially expressed genes were downregulated genes.


### 2.1.3. The Similarity Computation of the Gene Expression Profile

The up- and downregulated genes were used to calculate the gene expression profile similarity. Using up and downregulated genes as the base data, the gene expression profile similarity was automatically calculated in Cmap by the K-S algorithm [34, 36]. A similarity comparison yielded the similarity scores of the gene expression profiles of each small molecule drugs and TCM. Similarity scores fell between −1 and 1. If 0 ≤ similarity scores ≤ 1, the pharmacological action of a small molecule drug and TCM were similar, and a higher absolute value of the similarity score indicated a greater similarity; if −1 ≤ similarity scores ≤ 0, the pharmacological action of a small molecule drug and TCM were adverse, and a higher absolute value indicated less similarity.


### 2.1.4. Determination of the TCM Target Proteins

If the similarity score for the gene expression profiles of a small molecule drug and TCM was high, then their pharmacological action was similar. The target proteins of a small molecule drugs were considered TCM targets. This study only considered the top 10 small molecule drugs that had a definite pharmacological action and their target proteins were recorded in DrugBank version 4.3.


### 2.1.5. Data for the TCM Components

The components of a TCM formula were collected from TCMD [37] and TCMSP [38]. The components were supplemented and perfected by the literature in CNKI and PubMed (1979~2017). The name, structure, and SMILES string of a component was recorded. For components with synonyms, the repetitive components were deleted by the “full structure” algorithm in “ChemBioFinder for Office 12.0”.


### 2.1.6. Determination of Bioactive Components of a TCM

The three-dimensional structure was downloaded from the PDB (https://www.rcsb.org/pdb/home/home.do), and the structure that had active ligands and higher resolution was preferentially selected. The preprocessing of the target protein included the deletion of ligands, water, and redundant protein conformations; the completion of missing or incomplete residues; the addition of hydrogens; and the distribution of related charges. The amino acids in the target protein that interact with the ligand were selected and were defined as the active pocket. The structure of components was transformed into a three-dimensional structure, endowed with a CHARMM force field and protonated in accordance with the corresponding pH. Molecular docking was carried out by LibDock [39], and the parameter settings were as follows: the “Conformation Method” was “BEST,” the “Docking Preferences” was “High Quality,” and the other parameters were set to the default. With the “LibDock Score” as the reference, the components that had a score higher than the ligand and the ranked in the top 10 were considered the bioactive components. This information allowed us to identify the bioactive components of the TCM.


## 2.1.1. Gene Expression Profile Data

The Gene Expression Omnibus (GEO,https://www.ncbi.nlm.nih.gov/geo/) contains some gene expression profiles which were treated with components, herbs, and TCM formulae. These data were utilized to carry out TCM-related research. Connectivity Map (CMap, https://www.broadinstitute.org/cmap/) is a gene expression profile database related to small molecule drugs [34]. Cmap establishes the relations of small molecule drugs, genes, and diseases according to the gene expression differences in human tumor cell lines after treatment with small molecule drugs. By comparing the similarity of different gene expression profiles, Cmap is mainly applicable to the areas of drug development, such as drug repositioning. Different human tumor cell lines (HL60, MCF7, PC3, SKMEL5, and ssMCF7) were treated with small molecules drugs at different concentrations (10 nM, 100 nM, 1 μM, and 10 μM) for different times (6 h, 12 h). At present, Cmap contains data for 1309 small molecule drugs and more than 7000 gene expression profiles. Of these 1309 drugs, 556 drugs were recorded in DrugBank. Of these 556 drugs, 522 drugs had the data of target protein. This study chose gene expression profiles that had high similarity to TCMs with respect to the same cell lines types and platforms.


## 2.1.2. The Determination of Up and Downregulated Genes

The differentially expressed genes were determined using the bioinformatics toolbox of Matlab [35]. At-test and false discovery rate (FDR) of multiple hypothesis testing were performed on each gene. Significant differentially expressed genes were detected by random sample replacement (P<0.05, FDR ≤ 0.1). Up and downregulated genes were distinguished by the magnitude of fold change (FC). If FC ≥ 2, then the significant differentially expressed genes were up-regulated genes, and if FC ≤ 0.5, then the significant differentially expressed genes were downregulated genes.


## 2.1.3. The Similarity Computation of the Gene Expression Profile

The up- and downregulated genes were used to calculate the gene expression profile similarity. Using up and downregulated genes as the base data, the gene expression profile similarity was automatically calculated in Cmap by the K-S algorithm [34, 36]. A similarity comparison yielded the similarity scores of the gene expression profiles of each small molecule drugs and TCM. Similarity scores fell between −1 and 1. If 0 ≤ similarity scores ≤ 1, the pharmacological action of a small molecule drug and TCM were similar, and a higher absolute value of the similarity score indicated a greater similarity; if −1 ≤ similarity scores ≤ 0, the pharmacological action of a small molecule drug and TCM were adverse, and a higher absolute value indicated less similarity.


## 2.1.4. Determination of the TCM Target Proteins

If the similarity score for the gene expression profiles of a small molecule drug and TCM was high, then their pharmacological action was similar. The target proteins of a small molecule drugs were considered TCM targets. This study only considered the top 10 small molecule drugs that had a definite pharmacological action and their target proteins were recorded in DrugBank version 4.3.


## 2.1.5. Data for the TCM Components

The components of a TCM formula were collected from TCMD [37] and TCMSP [38]. The components were supplemented and perfected by the literature in CNKI and PubMed (1979~2017). The name, structure, and SMILES string of a component was recorded. For components with synonyms, the repetitive components were deleted by the “full structure” algorithm in “ChemBioFinder for Office 12.0”.


## 2.1.6. Determination of Bioactive Components of a TCM

The three-dimensional structure was downloaded from the PDB (https://www.rcsb.org/pdb/home/home.do), and the structure that had active ligands and higher resolution was preferentially selected. The preprocessing of the target protein included the deletion of ligands, water, and redundant protein conformations; the completion of missing or incomplete residues; the addition of hydrogens; and the distribution of related charges. The amino acids in the target protein that interact with the ligand were selected and were defined as the active pocket. The structure of components was transformed into a three-dimensional structure, endowed with a CHARMM force field and protonated in accordance with the corresponding pH. Molecular docking was carried out by LibDock [39], and the parameter settings were as follows: the “Conformation Method” was “BEST,” the “Docking Preferences” was “High Quality,” and the other parameters were set to the default. With the “LibDock Score” as the reference, the components that had a score higher than the ligand and the ranked in the top 10 were considered the bioactive components. This information allowed us to identify the bioactive components of the TCM.


## 2.2. The Application of GEPSI on SWT

Si-Wu-Tang (SWT) is a well-known TCM formula and is prepared from four medicinal herbs includingRehmanniae Radix Praeparata (Rehmannia glutinosa Libosch.),Angelicae Sinensis Radix (Angelica sinensis (Oliv.) Diels),Paeoniae Radix Alba (Paeonia lactiflora Pall.), andChuanxiong Rhizoma (Ligusticum chuanxiong Hort.).SWT and its series of decoctions (i.e., the Xiang-Fu-Si-Wu decoction, and the Tao-Hong-Si-Wu decoction) have been widely used in clinical gynecology practice for blood stasis syndrome, such as primary dysmenorrheal, breast cancer, and other estrogen-related diseases [40–44]. ForSWT, this study applied the TCM bioactive components identification method based on the similarity of the gene expression profiles. In GEO, the number of gene expression profiles thatSWT (0.0256 mg/mL, 0.256 mg/mL, and 2.56 mg/mL) acted on, MCF-7, was GSE23610. GSE23610 was obtained on the GPL570 platform (HG-U133_Plus_2) [45]. A total of 3905 gene expression profiles were selected in Cmap for the same cell line (MCF-7) and platform (HG-U133_Plus_2). These gene expression profiles involved 1294 small molecule drugs. In addition, 98 components ofRehmanniae Radix Praeparata, 215 components ofAngelicae Sinensis Radix, 85 components ofPaeoniae Radix Alba, and 258 components ofChuanxiong Rhizoma were collected. The collected components can be seen in the Supplemental Information 1.


## 3. Results and Discussions

### 3.1. Up- and Downregulated Genes

AtSWT concentrations of 0.0256 mg/mL and 0.256 mg/mL, the expression of each gene did not obviously change, but when theSWT concentration was 2.56 mg/mL, the expression of each gene obviously changed. Therefore, the gene expression profile that was elicited bySWT(2.56 mg/mL) was chosen for follow-up research.At-test and false discovery rate (FDR) multiple hypothesis test were applied to each gene. A large number of genes were found to have biological differences; 442 genes were up-regulated and 189 were downregulated (Supplemental Information 2).


### 3.2. The Small Molecule Drugs with High Similarity Scores

After the similarity was computed, the similarity scores of the gene expression profiles for 1294 small molecular drugs andSWT were obtained. The top ten small molecule drugs that had explicit pharmacological action and their target proteins contained in DrugBank were retained. The results are shown in Table 1.Table 1
Top ten small molecule drugs that have higher similarity scores with the gene expression profile ofSWT.
Cmap name Mean Enrichment P value Percent nonnull Group in DrugBank Phenoxybenzamine 0.955 1.000 0.00000 100 Approved Diethylstilbestrol 0.703 0.983 0.00004 100 Approved Anisomycin 0.695 0.990 0.00012 100 Experimental Equilin 0.609 0.970 0.00004 100 Approved Digoxigenin 0.609 0.950 0.00012 100 Approved Resveratrol 0.413 0.855 0.00002 100 Experimental/investigational Estradiol 0.389 0.774 0.00000 94 Approved/investigational Prochlorperazine 0.376 0.674 0.00012 88 Approved Genistein 0.352 0.686 0.00000 81 Investigational Thioridazine 0.344 0.626 0.00004 72 Approved


### 3.3. The Primary Pharmacological Actions of the Top Ten Small Molecule Drugs

The primary pharmacological actions of the top ten small molecule drugs in Table1 were investigated in the literature. The results are shown in Table 2.Table 2
The primary pharmacological action of the top ten small molecule drugs.
Drug Primary pharmacological action References Phenoxybenzamine Hypertension; mediated peripheral vasodilation; pheochromocytoma [4–6] Diethylstilbestrol Menopausal syndrome; postmenopausal osteoporosis; breast cancer [7–9] Anisomycin Immunosuppression [10, 11] Equilin Postmenopausal osteoporosis [12] Digoxigenin Hypertension; valvular heart disease; cell proliferation inhibition, such as breast cancer, etc. [13–16] Resveratrol Anticancer; immunoregulation [17–20] Estradiol Metastatic breast cancer [21–23] Prochlorperazine Antinausea after chemotherapy [24, 25] Genistein Anticancer; radiation protection; immunoregulation [26–29] Thioridazine Anticancer; antischizophrenia [30–33]Table2 shows that the pharmacological actions of the ten small molecule drugs all involve disease caused by an unbalanced estrogen level. Except for phenoxybenzamine and equilin, the primary pharmacological action of the remaining eight small molecule drugs was closely related to the treatment of breast cancer. Most of the eight drugs have an estrogenic effect. For example, resveratrol and genistein are phytoestrogens; estradiol is a natural estrogen that is secreted by mature ovarian follicles; diethylstilbestrol is a kind of estrogen that is a common endocrine medication for breast cancer. We often think that the occurrence of breast cancer is related to an excessive or imbalanced level of estrogen in the female body [46], and the regulation of immunity is an important method for the treatment of cancer. To summarize,SWT may have an anti-breast cancer effect because it has a high similarity score with the top ten small molecule drugs.


### 3.4. The Target Proteins That Were Used in Molecular Docking

Of the top ten small molecule drugs, only the target proteins of four drugs have a three-dimensional structure in the PDB with a high resolution and corresponding bioactive ligands. Therefore, the target proteins of these four drugs were used for molecular docking studies (Table3).Table 3
The target proteins of small molecule drugs that were used for molecular docking studies.
Drug Mode of action The Uniprot number Abbreviation of protein The PDB number Diethylstilbestrol Agonist Q92731 ESR2 1QKM Agonist P03372 ESR1 1X7R Unknown O75469 NR1I2 4X1F  Resveratrol Unknown P03372 ESR1 1QKT Unknown Q92731 ESR2 4J24  Estradiol Agonist P16083 NQO2 1SG0 Agonist P68400 CSNK2A1 4RLL  Genistein Unknown P03372 ESR1 3ERD Unknown P62508 ESRRG 2GPP


### 3.5. Bioactive Components of SWT

After molecular docking, the components whose LibDock score were higher than that of the ligand were identified as bioactive components (the LibDock score of the ligand is shown in Supplemental Information3). This study identified 46 bioactive components, including 12 components inPaeoniae Radix Alba, 4 components inChuanxiong Rhizoma, 6 components inAngelicae Sinensis Radix, and 24 components inRehmanniae Radix Praeparata (the results are shown in Table 4). The 46 bioactive components act on 9 target proteins.Table 4
The 46 bioactive components ofSWT.
Herb Compound PubChemCID LibDock score Target protein Shudi Catalpol 91520 130.006 1QKM Catalpol 91520 127.034 1X7R Catalpol 91520 119.571 3ERD Catalpol 91520 120.630 4J24 Isoacteoside 6476333 176.387 4RLL Isoacteoside 6476333 136.387 ISGO Leucosceptoside A 10394343 134.596 ISGO aucubin 91458 119.278 3ERD Cistanoside F 44429870 143.468 1QKT Cistanoside F 44429870 127.692 ISGO Melittoside 11968737 151.290 4X1F Acteoside 5281800 178.203 4RLL Acteoside 5281800 150.245 ISGO Martynoside 44429856 148.239 ISGO Forsythoside A 45358127 178.534 4RLL Forsythoside A 45358127 146.041 ISGO Ajugoside 179611 119.032 4J24 Jioglutoside B 11968648 140.384 4X1F Jioglutoside B 11968648 123.671 ISGO Jioglutoside A 11968647 116.065 3ERD Jionoside D 9895632 130.081 ISGO Echinacoside 5281771 134.559 ISGO Dihydrocatalpol 5705531 128.987 1QKM Glutinoside 24884124 131.718 1X7R Glutinoside 24884124 122.022 3ERD Rehmannioside C 6325883 149.106 1QKT Rehmannioside C 6325883 137.092 4J24 Rehmannioside C 6325883 153.335 4X1F Rehmannioside C 6325883 135.653 ISGO Rehmannioside B 6325882 145.167 1QKT Rehmannioside B 6325882 143.713 4X1F Rehmannioside B 6325882 126.837 ISGO Rehmannioside A 86287413 142.582 1QKT Rehmannioside A 86287413 138.007 4X1F Rehmannioside A 86287413 138.555 ISGO Daucosterol 5742590 138.157 4X1F Daucosterol 5742590 135.135 ISGO Geniposide 107848 129.014 1X7R Forsythiaside 5281773 177.534 4RLL Forsythiaside 5281773 148.451 ISGO 8-Epiloganic acid 158144 128.867 1X7R 8-Epiloganic acid 158144 121.873 3ERD sec-Hydroxyaeginetic acid 15693867 129.657 1QKM sec-Hydroxyaeginetic acid 15693867 139.930 1QKT sec-Hydroxyaeginetic acid 15693867 119.597 3ERD sec-Hydroxyaeginetic acid 15693867 119.688 4J24 sec-Hydroxyaeginetic acid 15693867 177.998 4RLL sec-Hydroxyaeginetic acid 15693867 149.140 4X1F sec-Hydroxyaeginetic acid 15693867 149.648 ISGO  Danggui Orientin 5281675 126.131 4J24 Orientin 5281675 128.047 ISGO Trigalacturonic acid 3641243 141.911 4X1F Sphingomyelin 52931203 121.153 3ERD Daucosterol 5742590 138.157 4X1F Daucosterol 5742590 135.135 ISGO Senkyunolide 91731751 170.306 4RLL 1,1,5-Trimethyl-2-formyl-cyclohea-2,5-diene-4-one None 140.619 1QKT 1,1,5-Trimethyl-2-formyl-cyclohexa-2,5-diene-4-one None 142.328 2GPP 1,1,5-Trimethyl-2-formyl-cyclohexa-2,5-diene-4-one None 123.128 4J24 1,1,5-Trimethyl-2-formyl-cyclohexa-2,5-diene-4-one None 126.443 ISGO 1,1,5-Trimethyl-2-formyl-cyclohexa-2,5-diene-4-one None 125.665 ISGO  Chuanxiong Heptadecanoic acid 10429233 127.735 1X7R Heptadecanoic acid 10429233 124.190 4J24 Butyraldehyde 14900 122.060 3ERD Daucosterol 5742590 138.157 4X1F Daucosterol 5742590 135.135 ISGO Methyl 3,4-dimethylbenzoate 7852 140.619 1QKT Methyl 3,4-dimethylbenzoate 7852 123.128 4J24 Methyl 3,4-dimethylbenzoate 7852 125.665 ISGO  Baishao Paeonianin E 44256843 141.990 ISGO Peonin 44253993 135.516 4X1F Peonin 44253993 124.317 ISGO Stigmasterol Glucoside 6440962 134.691 ISGO Paeonoside 52952637 172.449 4RLL Paeonoside 52952637 133.551 ISGO Paeoniflorin 442534 149.657 1QKT Paeoniflorin 442534 126.988 1X7R Paeoniflorin 442534 122.872 4J24 Paeoniflorin 442534 138.060 4X1F Oxypaeoniflorin 429559 146.127 1QKT Oxypaeoniflorin 429559 141.528 4X1F Methyl linolelaidate 5362793 116.328 3ERD Galloylpaeoniflorin 46882879 137.131 ISGO Dotriacontane 11008 127.379 ISGO Acetytastragaloside 5282102 129.735 ISGO Albiflorin 162355 143.965 1QKT 1,2,3,6-Tetragalloylglucose 73178 137.874 ISGO


### 3.6. Verification of the Reliability of GEPSI

Table4 shows thatSWT has anti-breast cancer activity through 46 bioactive components and these components acted on 9 target proteins. Most of the bioactive components, such as catalpol, verbascoside, and paeoniflorin, acted on multiple targets. The types and numbers of targets that the bioactive components acted on were diverse. If we only use one or a few proteins as targets, the bioactive components retrieved may be not complete. For example, 19 bioactive components, such as catalpol, aucubin, and melittoside will be not be retrieved when ISGO is the target protein. Therefore, we should consider all the targets that TCM could affect when a comprehensive bioactive components screening is carried out.The proteins in Table4 were the targets of resveratrol, diethylstilbestrol, estradiol, and genistein. According to the literature, these four components were estrogen or had an estrogen-like effect, and resveratrol and genistein had potent anti-breast cancer activity. Hence, the 46 bioactive components ofSWT may also have anti-breast cancer and estrogen-like effects. Studies showed that catalpol, a DNA polymerase inhibitor, inhibited the proliferation of six human solid tumor cell lines by acting during the G0-G1 period. The naturally occurring iridoid catalpol is a Taq DNA polymerase inhibitor. However, the formation of analogs bearing one to three silyl ether groups led to antiproliferative compounds against a panel of six human solid tumor cell lines, with GI50 values in the range 1.8–4.8 μM. Cell cycle studies revealed an arrest of the G0/G1 phase that was consistent with DNA polymerase inhibition [47]. Orientin could suppress the proliferation of MCF-7 and present specific dose-response relationships [48, 49]. Paeoniflorin could suppress the proliferation and spread of breast cancer cells through the Notch-1 pathway [50]. The effect of trigalacturonic acid on the proliferation inhibition of Bcap-3 in breast cancer cells was better and it may have an anti-breast cancer potential [51]. To summarize, we found some bioactive components did have the same effect as small molecule drugs via a literature research which indicated the reliability of the bioactive components identification method based on the similarity of gene expression profiles.


## 3.1. Up- and Downregulated Genes

AtSWT concentrations of 0.0256 mg/mL and 0.256 mg/mL, the expression of each gene did not obviously change, but when theSWT concentration was 2.56 mg/mL, the expression of each gene obviously changed. Therefore, the gene expression profile that was elicited bySWT(2.56 mg/mL) was chosen for follow-up research.At-test and false discovery rate (FDR) multiple hypothesis test were applied to each gene. A large number of genes were found to have biological differences; 442 genes were up-regulated and 189 were downregulated (Supplemental Information 2).


## 3.2. The Small Molecule Drugs with High Similarity Scores

After the similarity was computed, the similarity scores of the gene expression profiles for 1294 small molecular drugs andSWT were obtained. The top ten small molecule drugs that had explicit pharmacological action and their target proteins contained in DrugBank were retained. The results are shown in Table 1.Table 1
Top ten small molecule drugs that have higher similarity scores with the gene expression profile ofSWT.
Cmap name Mean Enrichment P value Percent nonnull Group in DrugBank Phenoxybenzamine 0.955 1.000 0.00000 100 Approved Diethylstilbestrol 0.703 0.983 0.00004 100 Approved Anisomycin 0.695 0.990 0.00012 100 Experimental Equilin 0.609 0.970 0.00004 100 Approved Digoxigenin 0.609 0.950 0.00012 100 Approved Resveratrol 0.413 0.855 0.00002 100 Experimental/investigational Estradiol 0.389 0.774 0.00000 94 Approved/investigational Prochlorperazine 0.376 0.674 0.00012 88 Approved Genistein 0.352 0.686 0.00000 81 Investigational Thioridazine 0.344 0.626 0.00004 72 Approved


## 3.3. The Primary Pharmacological Actions of the Top Ten Small Molecule Drugs

The primary pharmacological actions of the top ten small molecule drugs in Table1 were investigated in the literature. The results are shown in Table 2.Table 2
The primary pharmacological action of the top ten small molecule drugs.
Drug Primary pharmacological action References Phenoxybenzamine Hypertension; mediated peripheral vasodilation; pheochromocytoma [4–6] Diethylstilbestrol Menopausal syndrome; postmenopausal osteoporosis; breast cancer [7–9] Anisomycin Immunosuppression [10, 11] Equilin Postmenopausal osteoporosis [12] Digoxigenin Hypertension; valvular heart disease; cell proliferation inhibition, such as breast cancer, etc. [13–16] Resveratrol Anticancer; immunoregulation [17–20] Estradiol Metastatic breast cancer [21–23] Prochlorperazine Antinausea after chemotherapy [24, 25] Genistein Anticancer; radiation protection; immunoregulation [26–29] Thioridazine Anticancer; antischizophrenia [30–33]Table2 shows that the pharmacological actions of the ten small molecule drugs all involve disease caused by an unbalanced estrogen level. Except for phenoxybenzamine and equilin, the primary pharmacological action of the remaining eight small molecule drugs was closely related to the treatment of breast cancer. Most of the eight drugs have an estrogenic effect. For example, resveratrol and genistein are phytoestrogens; estradiol is a natural estrogen that is secreted by mature ovarian follicles; diethylstilbestrol is a kind of estrogen that is a common endocrine medication for breast cancer. We often think that the occurrence of breast cancer is related to an excessive or imbalanced level of estrogen in the female body [46], and the regulation of immunity is an important method for the treatment of cancer. To summarize,SWT may have an anti-breast cancer effect because it has a high similarity score with the top ten small molecule drugs.


## 3.4. The Target Proteins That Were Used in Molecular Docking

Of the top ten small molecule drugs, only the target proteins of four drugs have a three-dimensional structure in the PDB with a high resolution and corresponding bioactive ligands. Therefore, the target proteins of these four drugs were used for molecular docking studies (Table3).Table 3
The target proteins of small molecule drugs that were used for molecular docking studies.
Drug Mode of action The Uniprot number Abbreviation of protein The PDB number Diethylstilbestrol Agonist Q92731 ESR2 1QKM Agonist P03372 ESR1 1X7R Unknown O75469 NR1I2 4X1F  Resveratrol Unknown P03372 ESR1 1QKT Unknown Q92731 ESR2 4J24  Estradiol Agonist P16083 NQO2 1SG0 Agonist P68400 CSNK2A1 4RLL  Genistein Unknown P03372 ESR1 3ERD Unknown P62508 ESRRG 2GPP


## 3.5. Bioactive Components of SWT

After molecular docking, the components whose LibDock score were higher than that of the ligand were identified as bioactive components (the LibDock score of the ligand is shown in Supplemental Information3). This study identified 46 bioactive components, including 12 components inPaeoniae Radix Alba, 4 components inChuanxiong Rhizoma, 6 components inAngelicae Sinensis Radix, and 24 components inRehmanniae Radix Praeparata (the results are shown in Table 4). The 46 bioactive components act on 9 target proteins.Table 4
The 46 bioactive components ofSWT.
Herb Compound PubChemCID LibDock score Target protein Shudi Catalpol 91520 130.006 1QKM Catalpol 91520 127.034 1X7R Catalpol 91520 119.571 3ERD Catalpol 91520 120.630 4J24 Isoacteoside 6476333 176.387 4RLL Isoacteoside 6476333 136.387 ISGO Leucosceptoside A 10394343 134.596 ISGO aucubin 91458 119.278 3ERD Cistanoside F 44429870 143.468 1QKT Cistanoside F 44429870 127.692 ISGO Melittoside 11968737 151.290 4X1F Acteoside 5281800 178.203 4RLL Acteoside 5281800 150.245 ISGO Martynoside 44429856 148.239 ISGO Forsythoside A 45358127 178.534 4RLL Forsythoside A 45358127 146.041 ISGO Ajugoside 179611 119.032 4J24 Jioglutoside B 11968648 140.384 4X1F Jioglutoside B 11968648 123.671 ISGO Jioglutoside A 11968647 116.065 3ERD Jionoside D 9895632 130.081 ISGO Echinacoside 5281771 134.559 ISGO Dihydrocatalpol 5705531 128.987 1QKM Glutinoside 24884124 131.718 1X7R Glutinoside 24884124 122.022 3ERD Rehmannioside C 6325883 149.106 1QKT Rehmannioside C 6325883 137.092 4J24 Rehmannioside C 6325883 153.335 4X1F Rehmannioside C 6325883 135.653 ISGO Rehmannioside B 6325882 145.167 1QKT Rehmannioside B 6325882 143.713 4X1F Rehmannioside B 6325882 126.837 ISGO Rehmannioside A 86287413 142.582 1QKT Rehmannioside A 86287413 138.007 4X1F Rehmannioside A 86287413 138.555 ISGO Daucosterol 5742590 138.157 4X1F Daucosterol 5742590 135.135 ISGO Geniposide 107848 129.014 1X7R Forsythiaside 5281773 177.534 4RLL Forsythiaside 5281773 148.451 ISGO 8-Epiloganic acid 158144 128.867 1X7R 8-Epiloganic acid 158144 121.873 3ERD sec-Hydroxyaeginetic acid 15693867 129.657 1QKM sec-Hydroxyaeginetic acid 15693867 139.930 1QKT sec-Hydroxyaeginetic acid 15693867 119.597 3ERD sec-Hydroxyaeginetic acid 15693867 119.688 4J24 sec-Hydroxyaeginetic acid 15693867 177.998 4RLL sec-Hydroxyaeginetic acid 15693867 149.140 4X1F sec-Hydroxyaeginetic acid 15693867 149.648 ISGO  Danggui Orientin 5281675 126.131 4J24 Orientin 5281675 128.047 ISGO Trigalacturonic acid 3641243 141.911 4X1F Sphingomyelin 52931203 121.153 3ERD Daucosterol 5742590 138.157 4X1F Daucosterol 5742590 135.135 ISGO Senkyunolide 91731751 170.306 4RLL 1,1,5-Trimethyl-2-formyl-cyclohea-2,5-diene-4-one None 140.619 1QKT 1,1,5-Trimethyl-2-formyl-cyclohexa-2,5-diene-4-one None 142.328 2GPP 1,1,5-Trimethyl-2-formyl-cyclohexa-2,5-diene-4-one None 123.128 4J24 1,1,5-Trimethyl-2-formyl-cyclohexa-2,5-diene-4-one None 126.443 ISGO 1,1,5-Trimethyl-2-formyl-cyclohexa-2,5-diene-4-one None 125.665 ISGO  Chuanxiong Heptadecanoic acid 10429233 127.735 1X7R Heptadecanoic acid 10429233 124.190 4J24 Butyraldehyde 14900 122.060 3ERD Daucosterol 5742590 138.157 4X1F Daucosterol 5742590 135.135 ISGO Methyl 3,4-dimethylbenzoate 7852 140.619 1QKT Methyl 3,4-dimethylbenzoate 7852 123.128 4J24 Methyl 3,4-dimethylbenzoate 7852 125.665 ISGO  Baishao Paeonianin E 44256843 141.990 ISGO Peonin 44253993 135.516 4X1F Peonin 44253993 124.317 ISGO Stigmasterol Glucoside 6440962 134.691 ISGO Paeonoside 52952637 172.449 4RLL Paeonoside 52952637 133.551 ISGO Paeoniflorin 442534 149.657 1QKT Paeoniflorin 442534 126.988 1X7R Paeoniflorin 442534 122.872 4J24 Paeoniflorin 442534 138.060 4X1F Oxypaeoniflorin 429559 146.127 1QKT Oxypaeoniflorin 429559 141.528 4X1F Methyl linolelaidate 5362793 116.328 3ERD Galloylpaeoniflorin 46882879 137.131 ISGO Dotriacontane 11008 127.379 ISGO Acetytastragaloside 5282102 129.735 ISGO Albiflorin 162355 143.965 1QKT 1,2,3,6-Tetragalloylglucose 73178 137.874 ISGO


## 3.6. Verification of the Reliability of GEPSI

Table4 shows thatSWT has anti-breast cancer activity through 46 bioactive components and these components acted on 9 target proteins. Most of the bioactive components, such as catalpol, verbascoside, and paeoniflorin, acted on multiple targets. The types and numbers of targets that the bioactive components acted on were diverse. If we only use one or a few proteins as targets, the bioactive components retrieved may be not complete. For example, 19 bioactive components, such as catalpol, aucubin, and melittoside will be not be retrieved when ISGO is the target protein. Therefore, we should consider all the targets that TCM could affect when a comprehensive bioactive components screening is carried out.The proteins in Table4 were the targets of resveratrol, diethylstilbestrol, estradiol, and genistein. According to the literature, these four components were estrogen or had an estrogen-like effect, and resveratrol and genistein had potent anti-breast cancer activity. Hence, the 46 bioactive components ofSWT may also have anti-breast cancer and estrogen-like effects. Studies showed that catalpol, a DNA polymerase inhibitor, inhibited the proliferation of six human solid tumor cell lines by acting during the G0-G1 period. The naturally occurring iridoid catalpol is a Taq DNA polymerase inhibitor. However, the formation of analogs bearing one to three silyl ether groups led to antiproliferative compounds against a panel of six human solid tumor cell lines, with GI50 values in the range 1.8–4.8 μM. Cell cycle studies revealed an arrest of the G0/G1 phase that was consistent with DNA polymerase inhibition [47]. Orientin could suppress the proliferation of MCF-7 and present specific dose-response relationships [48, 49]. Paeoniflorin could suppress the proliferation and spread of breast cancer cells through the Notch-1 pathway [50]. The effect of trigalacturonic acid on the proliferation inhibition of Bcap-3 in breast cancer cells was better and it may have an anti-breast cancer potential [51]. To summarize, we found some bioactive components did have the same effect as small molecule drugs via a literature research which indicated the reliability of the bioactive components identification method based on the similarity of gene expression profiles.


## 4. Conclusion

This method was more accurate with the protein that TCM actually acted on as the target, and the result was more comprehensive than a determination of the target protein according to disease-related target databases and signal transduction networks. For example, there are 74 breast cancer-related targets in the Therapeutic Target Database (TTD), including the estrogen receptor (ER), the vascular endothelial growth factor receptor 1 (VEGFR1), and the epidermal growth factor receptor (EGFR). However, no evidence was available to support the selection of these proteins as targets. This study identified the target protein thatSWT actually acted on by a gene expression similarity comparison, identified all the bioactive components ofSWT by molecular docking, and then verified the reliability of this method through a literature investigation. GEPSI could serve as a rapid and effective method for the identification of TCM bioactive components. Although some time is necessary to perfect related databases, such as components of TCM and the target protein structure of small drugs, we believe the data that used in GEPSI will be more complete, and the results will be more accurate with the development of chemical informatics and bioinformatics.Meanwhile, this study has also revealed thatSWT had anti-breast cancer efficacy. However, there have been no studies of these effects. The Tao-hong Si-wu Decoction, a derivative formula, was proved to influence the upper limb swelling after breast cancer surgery and the quality of a chemotherapy patient’s life [52, 53]. Research has shown thatPaeoniae Radix Alba,Chuanxiong Rhizoma,Rehmanniae Radix Praeparata,andSWT have plant estrogen-like effects, but the bioactive components have not been identified [54]. The above studies indirectly illustrate the rationality thatSWT has an anti-breast cancer effect. That is to say, GEPSI also can be used for drug repositioning. Now that the bioactive components have been identified, we can control the quality of the individual herbs. We can also design an anti-breast cancer drug combination based on the bioactive components inSWT.



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*Source: 6935350-2018-03-06.xml*