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Article

Investigating Key Targets of Dajianzhong Decoction for Treating Crohn’s Disease Using Weighted Gene Co-Expression Network

by
Yi Zhao
1,
Shengpeng Wang
1,
Yuanjia Hu
1,2,* and
Yitao Wang
1,*
1
Macau Centre for Research and Development in Chinese Medicine, State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao 999078, China
2
DPM, Faculty of Health Sciences, University of Macau, Macao 999078, China
*
Authors to whom correspondence should be addressed.
Processes 2023, 11(1), 112; https://doi.org/10.3390/pr11010112
Submission received: 9 November 2022 / Revised: 23 December 2022 / Accepted: 27 December 2022 / Published: 31 December 2022
(This article belongs to the Special Issue Medicinal and Pharmaceutical Development of Herbal Medicines and Natural Products)
Browse Figures
Versions Notes

Abstract

:
Background: Crohn’s disease (CD) is an inflammatory bowel disease, cases of which have substantially increased in recent years. The classical formula Dajianzhong decoction (DD, Japanese: Daikenchuto) is often used to treat CD, but few studies have evaluated related therapeutic mechanisms. In this study, we investigated the potential targets and mechanisms of DD used for treating CD at the molecular level through the weighted gene co-expression network. Methods: The main chemical components of the three DD herbs (Zanthoxylum bungeanum Maxim., Zingiber officinale (Willd.) Rosc., and Ginseng Radix et Rhizoma) were searched for using the HERB database. The targets for each component were identified using the SwissTargetPrediction and HERB databases, whereas the disease targets for CD were retrieved from the GeneCards and DisGeNET databases. The functional enrichment analysis was performed on the common targets of DD and CD. High-throughput sequencing data for CD patients were retrieved from the Gene Expression Omnibus database, and WGCNA was performed to identify the key targets. The association between the key targets and DD ingredients was verified using molecular docking. Results: By analyzing the interaction targets between DD and CD, 196 overlapping genes were identified. The enrichment results indicated that the PI3K-AKT, TNF, MAPK, and IL-17 signaling pathways influenced the mechanism of action of DD in counteracting CD. Combined with WGCNA, four differentially expressed genes (SLC6A4, NOS2, SHBG, and ABCB1) and their corresponding 24 compounds were closely related to the occurrence of CD. Conclusions: By integrating gene co-expression network analysis, this study preliminarily reveals the internal molecular mechanism of DD in treating CD from a systematic perspective, validated by molecular docking. However, these findings require further validation.

1. Introduction

Crohn’s disease (CD), a major type of inflammatory bowel disease (IBD), is a chronic progressive inflammatory disease induced by multiple genetic and environmental factors, and it has rapidly become a public health concern across the world [1,2]. Over past decades, CD was more frequent in the Western world, with higher prevalence and incidence rates [3]. Unfortunately, with increasing industrialization, CD incidence has increased dramatically in Asian countries, particularly in China, Korea, and Japan [4,5]. Generally, CD treatment includes glucocorticoids, immunosuppressants, 5-aminosalicylic acid (5-ASA), antitumor necrosis factor-α (TNF-α), and surgery [1,6,7]. However, it remains challenging to cure CD with these therapies. For instance, glucocorticoids effectively relieve CD, but their long-term use can trigger osteoporosis, diabetes, and hypertensive disorders and increase the risk of infection [8]. Although mesalamine is one of the most commonly used drugs for CD, its effects in induction and maintenance is still uncertain [9,10]. Additionally, surgery is a strategy frequently used to treat patients with CD, but recurrence always occurs [11,12]. As a result, exploring the new therapies with clinical benefits is essential for CD treatment. Numerous studies have demonstrated that natural products control CD more safely and can be regarded as a long-term therapeutic approach [13,14,15,16]. The major mechanisms include the inhibition of inflammatory cytokines, such as TNF-α, IL-1, IL-6, iNOS, and PPAR-γ, and PGE, which serves as a potential therapeutic method with clinical benefits for CD treatment [17,18].
The Dajianzhong decoction (DD; Daikenchuto), a classical prescription and Kampo medicine, consists of Zanthoxylum bungeanum Maxim. (ShuJiao), Zingiber officinale (Willd.) Rosc. (GanJiang), and Ginseng Radix et Rhizoma (RenShen) [19]. This decoction warms the body, disperses cold, and relieves pain. Moreover, it is commonly used in clinical practice to treat a variety of digestive disorders, such as abdominal pain, bloating, and constipation [20,21,22,23]. DD has prevented CD recurrence in animal models by boosting the release of endogenous adrenomedullin, lowering inflammation, and efficiently promoting blood flow in the ischemic region of the colon [24,25,26,27]. In a trinitrobenzenesulfonic acid (TNBS)-induced CD rat model, DD inhibited TGF-1 and HSP49 expression, reducing intestinal inflammation [28]. Furthermore, the active components in DD, such as hydroxy α-sanshool and 6-gingerol, can activate TRPA channels, resulting in antifibrotic actions in the intestine [29]. In a clinical study, patients were administered 5-ASA, azathioprine, and DD, and the reoperation rate was assessed after three years [30]. Only 44 of the 258 patients in the study required reoperation after three years, indicating that DD can be used as a clinically effective maintenance therapy for treating CD and preventing reoperation after surgery [30]. In addition, by altering the levels of adrenomedullin, DD can help relieve the digestive symptoms of individuals with CD [31]. These studies have shown that DD is effective for the treatment of CD. However, the specific mechanism has not been fully elucidated. Therefore, to address this issue, weighted gene co-expression network analysis (WGCNA) was applied in the present study to explain the mechanism of DD in the treatment of CD from a systematic perspective. The Gene Expression Omnibus (GEO) database contains numerous gene expression datasets [32], and WGCNA is a great tool to process gene expression profiling datasets and identify modules associated with clinical features to screen hub genes involved in disease onset and progression [33]. Hub genes are defined as genes with high correlation in candidate module [33]. In recent years, WGCNA has been frequently employed in several studies [34,35,36,37], especially in IBD [36,38,39].
In the present study, we investigated the potential active ingredients and targets of DD in the treatment of CD through WGCNA and network analysis methods. To begin with, we obtained the gene expression dataset of CD and used WGCNA to find the key modules and hub genes. Then, molecular docking was performed to investigate the interaction of potential targets and compounds. This study could be helpful to reveal the mechanism of DD for CD treatment and provide a new reference for the clinical practice of DD. A flowchart of the study is presented in Figure 1.

2. Materials and Methods

2.1. Data Collection and Differentially Expressed Gene (DEG) Analysis

The GSE102134 dataset published on March 1, 2019, in the GEO database of the National Library of Medicine, USA, based on the GPL6244 platform, was selected for this study [40]. In this study, new and late-diagnosed patients were selected as the CD group (n = 30) and healthy individuals (n = 12) as the blank control group, for a total of 42 samples. As this dataset does not detail the clinical characteristics of the patients, the presence or absence of CD was defined as a clinical characteristic as the basis for the subsequent study. The expression information of the genes in each sample was exported using the GEO2R [41] online tool. The probe ID was converted to gene symbols through the platform annotation file, and only the one with highest expression was retained when there were multiple probes for the same gene. Genes with p < 0.01 and log 2 | F o l d C h a n g e | > 1 were defined as DEGs and used for further studies. The ggplot2 package was installed and run to cluster and visualize DEGs from different samples. A heatmap of DEGs was visualized using the pheatmap package in RStudio (https://www.rstudio.com/, accessed on 9 January 2022).

2.2. Composition and Putative Targets of DD in Treating CD

The DD consists of three herbs: GanJiang, ShuJiao, and RenShen. The compounds of these three herbs were retrieved from the HERB database (http://herb.ac.cn/, accessed on 9 January 2022) [42] and searched using PubChem (https://pubchem.ncbi.nlm.nih.gov/, accessed on 9 January 2022) [43] to obtain the Simplified Molecular Input Line Entry System and spatial data format (SDF) structures. Potentially active compounds were screened using SwissADME (http://www.swissadme.ch/, accessed on 9 January 2022) [44], with a “high” gastrointestinal absorption and “yes” selected for more than three of the five filters of drug-likeness prediction [45,46]. Finally, these data were supplemented with blood intake components from the literature. The predicted targets of the compounds were obtained from the SwissTargetPrediction (http://www.swisstargetprediction.ch/, accessed on 9 January 2022) platform [47] after selecting “Homo sapiens” and setting a probability > 0.5, and the HERB database (target information from ≥2 databases). All the obtained targets were imported into the UniProt database (https://www.uniprot.org/, accessed on 9 January 2022) [48] for standardization. The keyword “Crohn’s disease” was searched within the databases GeneCards (https://www.genecards.org/, accessed on 9 January 2022) [49] and DisGeNET (https://www.genecards.org/, accessed on 9 January 2022) [50] to find related genes, with the conditions gene-disease score > 10 [51] and gene score > 0.1 [52], respectively. The overlapping targets of DD and CD were visualized using Venn diagrams.

2.3. WGCNA

WGCNA can be used to identify modules and hub genes [33]. First, we used the function “goodSamplesGenes” in the WGCNA package to build a sample tree to check for the outliers in all samples. If a sample had outliers, it had to be removed according to the cutting height. The Pearson coefficients of the genes were then calculated to construct a correlation matrix. After iterating with a set of soft threshold powers, the proper threshold was selected using the “pickSoftThreshold” function. The β power was used as the soft threshold parameter for constructing an adjacency matrix, which was then changed into a topological overlap matrix (TOM), approximating the scale-free network. Highly correlated genes in the co-expression network were clustered into the same module to generate a cluster dendrogram according to 1-TOM (dissimilarity).
Based on TOM dissimilarity, gene expressions are grouped using hierarchical clustering to select the smallest soft threshold β that can form a scale-free network, with the network type defined as unsigned. If the independence reaches 0.8, the soft threshold β can be selected based on the number of samples.
The association between the modules and clinical progression traits was determined using the Pearson coefficient of the phenotype and eigenvectors. This allowed us to identify the highly correlated modules. For each expression profile, gene significance (GS) was calculated as the correlation between individual genes and clinical traits, and module membership (MM) was defined as the correlation between the expression profile and each module eigengene. Once the modules of interest were selected, a scatterplot between GS and MM was generated to determine essential genes, and thresholds of cor.gene MM > 0.8 and cor.gene GS > 0.2 were established to screen hub genes in each module [53,54].

2.4. Molecular Docking

After the hub gene was located, the 3D crystal structure corresponding to each protein was identified from the Research Collaboratory for Structural Bioinformatics Protein Database (https://www.rcsb.org/, accessed on 9 January 2022) [55] as the receptor protein. Then, the compound structure was queried as the ligand using PubChem. After routine processing of protein receptors and ligands using AutoDock Tools software 1.5.7 [56], molecular docking was performed separately using AutoDock Vina [57] software to obtain binding energies, and the results were visualized using the Protein-Ligand Interaction Profiler [58].

3. Results

3.1. DD Related Compounds and Targets

A total of 768 components (Table S1) were screened from the three DD herbs, and 173 potential compounds (Table 1) were identified after standardization and ADME parameter screening. Among them, 57, 43, and 83 compounds were present in GanJiang, HuaJiao, and RenShen, respectively. Then, we inputted the structure of potential active ingredients to search for targets, and if the targets could not be obtained from the platform, they were supplemented from the database. Finally, the targets from both sources were normalized and deduplicated, and 493 targets were obtained.
A total of 895 targets were retrieved from GeneCards, 121 targets were retrieved from DisGeNET, and 862 genes were obtained after normalization. Finally, 196 targets were identified (Table S2) (Figure 2).

3.2. Functional Enrichment Analysis

To systematically elucidate the underlying biological functions of DD in mitigating CD, we conducted GO and KEGG pathway enrichment analysis for 196 decoction targets using DAVID database (https://david.ncifcrf.gov/, accessed on 9 January 2022). FDR < 0.05 was taken as the filtering criteria. As illustrated in Figure 3a, the regulation of cellular processes, responses to stimuli, positive regulation of biological processes, and responses to chemicals were all significantly enriched. For cellular components, the DD targets were enriched in cellular anatomical entities, intracellular and membrane-bound organelles, and cytoplasm. For molecular functions, the DD targets were enriched in protein, signaling receptor, and molecular binding. In addition, a variety of pathways played key roles in the PI3K-AKT, TNF, MAPK, and IL-17 signaling pathways (Figure 3b).

3.3. Co-Expression Network Construction and Module Analysis

A total of 44 samples from the GSE102134 dataset were employed for co-expression network construction and clustered using WGCNA to determine whether there were outliers in the samples. No outliers were found. Therefore, all the samples were used in the subsequent study. Next, the clustered samples were combined with clinical characteristics (control and Crohn’s groups), defined as a dichotomous variable, and indicated in red and white (Figure 4). In this study, a soft threshold of 10 was chosen based on the scale independence and mean connectivity (Figure 5). The gene expression values were clustered and analyzed using the two parameters of minModuleSize = 30 and mergeCutHeight = 0.25 (the degree of dissimilarity was less than 0.25, and the degree of similarity was higher than 0.75) in the blockwiseModules function, and a total of 14 modules were classified by different colors. For genes with poor co-expression, they were assigned to grey module and not used in the follow-up study (Figure 6a). Among the remaining 13 modules, turquoise contained the most genes (Figure 6b). The interactions between the 13 co-expression modules were analyzed (Figure 6c).

3.4. Identification of Key Modules and Targets by Combined Clinical Traits

After obtaining several modules, the relationships between the modules and clinical traits were calculated (Figure 7). The pink (r = 0.74, p = 1 × 10−8) and turquoise (r = 0.71, p = 7 × 10−8) modules with the highest correlation were selected as the key modules (Figure 6) in CD. Finally, GS and MM exhibited a significantly positive correlation with the pink (cor = 0.61, p = 2.3 × 10−13) and turquoise (cor = 0.62, p < 1 × 10−200) modules (Figure 8). According to the recommended criteria of MM > 0.8 and GS > 0.2, we obtained 42 hub genes from the pink module and 894 hub genes from the turquoise module. A total of 593 significant DEGs were identified, including 365 upregulated and 228 downregulated DEGs, and a heatmap was plotted for them (Figure 9a). A total of 172 DEGs were identified in the pink and turquoise modules (Figure 9b). After symbol normalization, intersections were taken with 196 previously obtained compound disease targets, resulting in five DEGs for DD treatment of CD: SHBG, ABCB1, CYP3A4, SLC6A4, and NOS2 (Figure 9c). Among them, CYP3A4, is a common enzyme involved in drug metabolism, so it was not discussed next in this study. An alluvial plot was generated to integrate the 24 potential DD ingredients for treating CD (Figure 10).

3.5. Molecular Docking Verification

Based on the CD-related targets in DD and hub genes from the WGCNA analysis, a molecular docking study was performed to thoroughly comprehend the possible interaction modes of 24 compounds and four key genes. These four potential target proteins and their corresponding components were docked using AutoDock Vina (Table 2). Figure 11 illustrates the ligand–protein interactions in the docking simulation. Estriol, a component of HuaJiao, forms one hydrogen bond with SLC6A4 and four hydrogen bonds with SHBG. ABCB1 forms four hydrogen bonds with quercetin, and NOS2 forms six hydrogen bonds with the ginsenoside Rg3.

4. Discussion

With the increasing incidence and prevalence in recent years, CD has caused a serious social and economic burden [10]. However, its pathogenesis remains unclear to date. Therefore, it is essential to explore safe and effective drugs against CD. The DD has been shown to exhibit therapeutic effects for the treatment of CD [30,31]. However, its specific mechanism in the treatment of CD has not been fully elucidated. Systematic biological strategy provides a perspective in understanding the biological principles systematically and establishes a relationship between drugs and disease [59]. WGCNA is a suitable method for complex data analysis that can be used to identify key targets [33]. Hence, in the present study, we utilized integrated bioinformatics approaches to screen the bioactive compounds and key DD targets for the treatment of CD. Our results revealed four target genes (SLC6A4, NOS2, SHBG, and ABCB1) and their corresponding compounds that are responsible for DD in treating CD. Molecular docking was used to predict the interactions between them, and the results demonstrated hydrogen bonding to be the main type of interaction. Among the compounds investigated, estriol, quercetin, and the ginsenoside Rg3 were found to exhibit the highest affinity with the respective target gene.
ABCB1 (MDR1) is a membrane-associated ATP-dependent efflux pump [60]. MDR1-deficient mice have spontaneously developed colitis in previous studies [61,62], suggesting that ABCB1 polymorphism leads to reduced or inactivated P-glycoprotein 1 activity [61]. Several studies have demonstrated that MDR1 polymorphisms can increase susceptibility to CD [63,64,65]. Another study showed that MDR1 was expressed in mouse Teff cells, where its expression was increased in the lamina propria of the small intestine and was highest in the ileum [66]. In contrast, Teff cells lacking MDR1 exhibited mucosal dysfunction in the ileum and transferred CD-like ileitis in Rag1−/− hosts. Furthermore, some patients with ileal CD showed a loss of MDR1 function. In conclusion, these data suggest that MDR1 is associated with CD [66]. NOS2 expression was upregulated in the colonic mucosa of patients with IBD and in peripheral blood mononuclear cells of patients with CD compared with the controls [67]. Alterations in the biosynthesis, release, and clearance of 5-hydroxytryptamine play key roles in gastrointestinal motility [68,69,70,71]. The 5-hydroxytryptamine chemotactic molecule promotes lymphocyte activation and the secretion of pro-inflammatory cytokines. Therefore, when it is released and binds to its receptor, the relevant action must be rapidly terminated by the serotonin reuptake transporter (SERT), which is expressed by serotonergic neurons and mucosal enterocytes [68,69,70,71]. Deficiency of the SERT gene (SLC6A4) transcripts can cause intestinal inflammation [71,72], and SLC6A4 gene polymorphisms are also associated with its translation and expression [73,74]. There is an inverse association between the plasma levels of SHBG and the risk of CD [75]. These results are consistent with our findings, which indicate that these four target genes play key roles in the mechanism of DD in treating CD.
KEGG analysis can be used to decipher the systematic gene function [76]. In this study, among the presented pathways, PI3K-AKT, TNF, MAPK, and IL-17 signaling pathways, which are inflammation-related pathways, were recognized as the important mechanisms responsible for the ability of DD to treat CD. The TNF signaling pathway exhibited the highest pathway enrichment. TNF-α is an important pro-inflammatory cytokine in the inflammatory process that causes CD [77], and it has been proven in vitro to be involved in the CD pathogenic processes, such as neutrophil accumulation, granuloma formation, and enhanced epithelial permeability [78,79]. Moreover, the TNF-α produced by CD4+ T cells is related to mucosal injury, which can perpetuate the inflammation cascade in the intestine [1,80,81]. Studies have indicated that in comparison with controls (patients without CD), TNF-α was increased in the stool of patients with active CD, although the difference in the serum levels of TNF-α was not significant [82,83]. Anti-TNF agents, such as infliximab, certolizumab pegol, and adalimumab, are used to treat CD in multiple ways, such as neutralization of TNF-α, reverse signaling, induction of apoptosis, and induction of antibody-dependent cell-mediated cytotoxicity [84]. However, the long-term use of anti-TNF agents causes various adverse effects, such as infection, skin damage, and decreased fertility [85,86], and is limited to patients who have not responded to steroids and thiopurines [1].
The use of PI3K-AKT pathway inhibitors is a good therapeutic method for the treatment of CD [87]. These play a key role in cell apoptosis, protein metabolism, and angiogenesis. Phosphatase and tensin homolog (PTEN) is a tumor suppressor gene that can negatively regulate the PI3K-AKT signaling pathway [88]. A study showed that the PI3K/AKT/mTOR signaling pathway is activated in CD when phosphorylated functional proteins are upregulated and its negative feedback factor, PTEN, is downregulated in peripheral CD4+ T cells and intestinal mucosal specimens. Thus, the activation of the PI3K/AKT/mTOR signaling pathway caused by the downregulation of PTEN is considered to be an important etiology of CD [89]. The nucleotide-binding oligomerization domain-containing protein 2 (NOD2) is regarded as a CD-susceptible gene [90]. Reactive oxygen species play a crucial role in the inflammatory process and are regulated by proteins associated with PI3K-AKT signaling [91]. The NOD2-mediated NFκ-B pathway is negatively regulated by the PI3K/AKT pathway, which can help to resolve the inflammatory response induced by NOD2 activation [92].
MAPK signaling can mediate cell growth, differentiation, and death by regulating the expression of many genes [93], and inhibition of the MAPK signaling pathway can reduce inflammation [94]. This pathway is related to the pathogenesis of CD. A clinical study showed that the agents inhibiting the activation of JNK and p38 MAPK reduced TNF production and effectively promoted ulcer healing without serious adverse effects [95]. The MAPK members p38 MAPK and p42/44 MAPK are also involved in the regulation of TNF production, as these can inhibit the release of TNF in monocytes [96,97].
The IL-17 family has many members, the major ones being IL-17A and IL-17F produced by Th17 cells [98]. Th17 cells infiltrate the inflammatory intestine of patients with CD and produce pro-inflammatory cytokines, such as IL-17, which contribute to the continuation of the inflammatory process [99]. In a previous study, compared with the controls, patients with CD showed an increased number of IL-17-producing T cells [98]. A TNBS-induced colitis model has shown that IL-17 plays a key role in colonic inflammation. The myeloperoxidase activity was reduced in the IL-17R knockout mice, and overexpression of the IL-17R IgG1 fusion protein could reduce the TNBS-induced colonic inflammation [100]. In a dextran sodium sulfate-induced colitis model of a clinical study, researchers discovered that IL-17F deficiency results in less severe colitis, but IL-17A deficiency results in a more severe illness [101].
In conventional network pharmacology analysis, when searching for the important targets, the targets are collected through the disease databases, and the network analysis is based on the topological analysis. In our study, we used the clinical high-throughput data as the data source and constructed the weighted co-expression network to include all correlated genes; this was scale-free and more consistent with the real biological characteristics, i.e., fewer nodes have higher connectivity, and a large number of nodes have lower connectivity. This study has some limitations. First, due to the complex metabolic process of natural products, the effects of different metabolites must be considered. Second, the findings of our study need to be experimentally validated in future studies evaluating the therapeutic effect of DD in treating CD. Third, the sample size for the WGCNA was not large enough. Hence, future studies should be conducted with more samples to increase the reliability of the findings.

5. Conclusions

This study employed reliable integrated bioinformatics tools to discover the important targets (SLC6A4, NOS2, SHBG, and ABCB1) involved in the potential anti-CD effects of DD. The extensive analysis revealed that the PI3K-AKT, TNF, MAPK, and IL-17 signaling pathways play crucial roles in this process. Thus, our findings provide a reference for further study of anti-CD effects of DD and for clinical application of DD.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pr11010112/s1, Figure S1: Sample clustering to detect outliers of GSE102134; Table S1: 768 compounds of DD; Table S2: 196 overlapping targets of Dajianzhong decoction and Crohn’s disease.

Author Contributions

Conceptualization, Y.Z., Y.H. and Y.W.; data curation, Y.Z.; formal analysis, Y.Z.; writing—original draft preparation, Y.Z.; writing—reviewing and editing, S.W. and Y.H.; supervision, Y.W. and Y.H.; funding acquisition, Y.W. and S.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by The Science and Technology Development Fund, Macau SAR (0006/2020/AKP).

Data Availability Statement

All related data was included in the article.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Flowchart of the current study’s methodology based on network analysis of DD.
Figure 1. Flowchart of the current study’s methodology based on network analysis of DD.
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Figure 2. Venn diagram of DD and CD targets. DD, Dajianzhong decoction; CD, Crohn’s disease.
Figure 2. Venn diagram of DD and CD targets. DD, Dajianzhong decoction; CD, Crohn’s disease.
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Figure 3. Enrichment analysis of potential targets. (a) GO function enrichment; (b) KEGG function enrichment.
Figure 3. Enrichment analysis of potential targets. (a) GO function enrichment; (b) KEGG function enrichment.
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Figure 4. Sample clustering to detect outliers of GSE102134.
Figure 4. Sample clustering to detect outliers of GSE102134.
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Figure 5. Determination of the soft threshold power of GSE102134. Scale-free fit index for various soft threshold powers (β) with the signed R2 (Y) and the soft threshold power (X). Mean connectivity (Y) for various soft powers, which is a strictly decreasing function of the power β (X).
Figure 5. Determination of the soft threshold power of GSE102134. Scale-free fit index for various soft threshold powers (β) with the signed R2 (Y) and the soft threshold power (X). Mean connectivity (Y) for various soft powers, which is a strictly decreasing function of the power β (X).
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Figure 6. Plot of WGCNA. (a) Dynamic tree cut based on a topological overlap measurement. (b) Number of genes in each module. (c) Network heatmap plot of randomly selected 400 targets. Each row and column represent a module and the genes of the module. Stronger correlations are indicated by progressive saturation in yellow.
Figure 6. Plot of WGCNA. (a) Dynamic tree cut based on a topological overlap measurement. (b) Number of genes in each module. (c) Network heatmap plot of randomly selected 400 targets. Each row and column represent a module and the genes of the module. Stronger correlations are indicated by progressive saturation in yellow.
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Figure 7. Heatmap of the module-clinical trait relationship. Clinical phenotypes are divided into two categories (control and Crohn’s disease). Each row represents a module eigengene (ME), and each column represents a clinical feature. The numbers in the first row in each cell are correlation values, and the numbers in the second row are p-values. The red cell color indicates that the module is positively correlated with the corresponding clinical feature, whereas blue indicates that the module is negatively correlated with the corresponding clinical feature. p < 0.05 is statistically significant.
Figure 7. Heatmap of the module-clinical trait relationship. Clinical phenotypes are divided into two categories (control and Crohn’s disease). Each row represents a module eigengene (ME), and each column represents a clinical feature. The numbers in the first row in each cell are correlation values, and the numbers in the second row are p-values. The red cell color indicates that the module is positively correlated with the corresponding clinical feature, whereas blue indicates that the module is negatively correlated with the corresponding clinical feature. p < 0.05 is statistically significant.
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Figure 8. Scatter diagram of gene significance for Crohn’s disease and module membership in the (a) pink and (b) turquoise modules. Gene significance (GS) is defined as the association of a single gene with external information, and module membership (MM) is calculated based on GS values. When a gene is highly correlated with a trait, the module in which this gene is located is also highly correlated with this trait.
Figure 8. Scatter diagram of gene significance for Crohn’s disease and module membership in the (a) pink and (b) turquoise modules. Gene significance (GS) is defined as the association of a single gene with external information, and module membership (MM) is calculated based on GS values. When a gene is highly correlated with a trait, the module in which this gene is located is also highly correlated with this trait.
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Figure 9. Screening of differentially expressed genes (DEGs) between Crohn’s disease and normal samples. (a) Heatmap of DEGs in Crohn’s’ disease. Each small square in the heatmap represents a gene; red indicates an up-regulated gene expression, blue indicates a down-regulated expression, and color shades indicate high or low gene expression rates. Each column represents the expression of genes in each sample, whereas each row represents the expression of each gene in different samples. The upper attribute clustering tree indicates the results of clustering analysis of multiple samples with different groups. (b) Venn plot of DEGs and significant modules, and (c) Venn plot of DEGs in modules and Dajianzhong decoction targets.
Figure 9. Screening of differentially expressed genes (DEGs) between Crohn’s disease and normal samples. (a) Heatmap of DEGs in Crohn’s’ disease. Each small square in the heatmap represents a gene; red indicates an up-regulated gene expression, blue indicates a down-regulated expression, and color shades indicate high or low gene expression rates. Each column represents the expression of genes in each sample, whereas each row represents the expression of each gene in different samples. The upper attribute clustering tree indicates the results of clustering analysis of multiple samples with different groups. (b) Venn plot of DEGs and significant modules, and (c) Venn plot of DEGs in modules and Dajianzhong decoction targets.
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Figure 10. Alluvial plots of herb-component-target network.
Figure 10. Alluvial plots of herb-component-target network.
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Figure 11. Molecular docking of the hub genes with its corresponding compounds. (a) NOS2, (b) SLC6A4, (c) SHBG, (d) ABCB1. The number indicates the PubChem ID.
Figure 11. Molecular docking of the hub genes with its corresponding compounds. (a) NOS2, (b) SLC6A4, (c) SHBG, (d) ABCB1. The number indicates the PubChem ID.
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Table
Table 1. Compounds information of Dajianzhong decoction.
Table 1. Compounds information of Dajianzhong decoction.
No.PubChem CIDIngredient NameSmilesHerbSource
11103spermineC(CCNCCCN)CNCCCNRSherb
22353berberineCOC1=C(C2=C[N+]3=C(C=C2C=C1)C4=CC5=C(C=C4CC3)OCO5)OCHJherb
32355MajudinCOC1=C2C=CC(=O)OC2=CC3=C1C=CO3HJherb
43026DBPCCCCOC(=O)C1=CC=CC=C1C(=O)OCCCCRS; GJherb
53314eugenolCOC1=C(C=CC(=C1)CC=C)OGJ; HJherb
63893lauric acidCCCCCCCCCCCC(=O)OGJherb
74276myristicinCOC1=CC(=CC2=C1OCO2)CC=CGJherb
84501nitidineC[N+]1=CC2=CC(=C(C=C2C3=C1C4=CC5=C(C=C4C=C3)OCO5)OC)OCHJherb
94970FumarineCN1CCC2=CC3=C(C=C2C(=O)CC4=C(C1)C5=C(C=C4)OCO5)OCO3RSherb
106760SkimmianinCOC1=C(C2=C(C=C1)C(=C3C=COC3=N2)OC)OCHJherb
117127methyleugenolCOC1=C(C=C(C=C1)CC=C)OCHJherb
128163MnkCCCCCCCCCC(=O)CGJ; HJherb
138193dodecanolCCCCCCCCCCCCORSherb
148194lauricaldehydeCCCCCCCCCCCC=OGJherb
1510227KokusagininCOC1=C(C=C2C(=C1)C(=C3C=COC3=N2)OC)OCHJherb
1610248elemicinCOC1=CC(=CC(=C1OC)OC)CC=CRSherb
1710545ascaridoleCC(C)C12CCC(C=C1)(OO2)CHJherb
1810742syringic acidCOC1=CC(=CC(=C1O)OC)C(=O)ORSherb
1910748AyapaninCOC1=CC2=C(C=C1)C=CC(=O)O2HJherb
2011092paeonolCC(=O)C1=C(C=C(C=C1)OC)ORSherb
2116306dibutyl oxalateCCCCOC(=O)C(=O)OCCCCRSherb
2223535linalyl anthranilateCC(=CCCC(C)(C=C)OC(=O)C1=CC=CC=C1N)CHJherb
2326690NN-DimethyldecanamideCCCCCCCCCC(=O)N(C)CRSherb
2431211zingeroneCC(=O)CCC1=CC(=C(C=C1)O)OCGJherb
25314042,6-ditertbutyl-4methyl phenolCC1=CC(=C(C(=C1)C(C)(C)C)O)C(C)(C)CRSherb
2637153DecamethylenediolC(CCCCCO)CCCCOGJherb
2765575α-cedrolCC1CCC2C13CCC(C(C3)C2(C)C)(C)ORSherb
2866654xanthoxylinCC(=O)C1=C(C=C(C=C1OC)OC)OHJherb
2968077tangeretinCOC1=CC=C(C=C1)C2=CC(=O)C3=C(O2)C(=C(C(=C3OC)OC)OC)OCHJherb
3068486suberosinCC(=CCC1=C(C=C2C(=C1)C=CC(=O)O2)OC)CHJherb
3172276(-)epicatechinC1C(C(OC2=CC(=CC(=C21)O)O)C3=CC(=C(C=C3)O)O)ORSherb
32809221,2,3-trimethoxy-5-methyl benzeneCC1=CC(=C(C(=C1)OC)OC)OCGJherb
3388692[(1R,2R,5R)-2-isopropyl-5-methyl-cyclohexyl] acetateCC1CCC(C(C1)OC(=O)C)C(C)CGJherb
34889443,5-dimethyl-4-methoxybenzoic acidCC1=CC(=CC(=C1OC)C)C(=O)ORSherb
3591457β-eudesmolCC12CCCC(=C)C1CC(CC2)C(C)(C)OGJherb
36921382-[(1R,3S,4S)-3-isopropenyl-4-methyl-4-vinylcyclohexyl]propan-2-olCC(=C)C1CC(CCC1(C)C=C)C(C)(C)OGJherb
3792812(+)-LedolCC1CCC2C1C3C(C3(C)C)CCC2(C)OHJherb
3893009L-Bornyl acetateCC(=O)OC1CC2CCC1(C2(C)C)CGJ; HJherb
3993135XanthorrhizolCC1=C(C=C(C=C1)C(C)CCC=C(C)C)OGJherb
4093484panaxytriolCCCCCCCC(C(CC#CC#CC(C=C)O)O)ORSherb
4194253VulgarinCC1C2CCC3(C(C2OC1=O)C(C=CC3=O)(C)O)CRSherb
4294334widdrolCC1(CCCC2(C1=CCC(CC2)(C)O)C)CRSherb
43943781-(4-Hydroxy-3-methoxyphenyl)decan-5-oneCCCCCCCC(=O)CCC1=CC(=C(C=C1)O)OCGJherb
4496943GirinimbinCC1=CC2=C(C3=C1OC(C=C3)(C)C)NC4=CC=CC=C42RSherb
45121712Ditertbutyl phthalateCC(C)(C)OC(=O)C1=CC=CC=C1C(=O)OC(C)(C)CRSherb
461268906-gingesulfonic acidCCCCCC(CC(=O)CCC1=CC(=C(C=C1)O)OC)S(=O)(=O)OGJherb
47128735isobaimuxinolCC1(C2CCC3(CCCC(C3(C2)O1)CO)C)CRSherb
481293944,7-Dihydroxy-5-methoxyl-6-methyl-8-formyl-flavanCC1=C(C(=C2C(=C1OC)C(CC(O2)C3=CC=CC=C3)O)C=O)ORSherb
49129429panaxacolCCCCCCCC(C(CC#CC#CC(=O)CC)O)ORSherb
50130756chloropanaxydiolCCCCCCCC1C(O1)CC#CC#CC(C(CCl)O)ORSherb
51133454panasinsanol aCC1(CC23C1CCC2(CCCC3(C)O)C)CRSherb
52147279OxychelerythrineCN1C2=C(C=CC3=CC4=C(C=C32)OCO4)C5=C(C1=O)C(=C(C=C5)OC)OCHJherb
53156660.beta.-Fenchyl acetate, exo-CC(=O)OC1C(C2CCC1(C2)C)(C)CGJherb
54158103Deoxygomisin ACC1CC2=CC3=C(C(=C2C4=C(C(=C(C=C4CC1C)OC)OC)OC)OC)OCO3RSherb
55161298InerminC1C2C(C3=C(O1)C=C(C=C3)O)OC4=CC5=C(C=C24)OCO5RSherb
561629526-GingerdioneCCCCCC(=O)CC(=O)CCC1=CC(=C(C=C1)O)OCGJherb
57168114(8)-GingerolCCCCCCCC(CC(=O)CCC1=CC(=C(C=C1)O)OC)OGJherb
58171548biotinC1C2C(C(S1)CCCCC(=O)O)NC(=O)N2RSherb
59171810dibutyl phthalateCCCCOC(=O)C1=CC=CC=C1C(=O)OCCCCRSherb
60185914DihydroresveratrolC1=CC(=CC=C1CCC2=CC(=CC(=C2)O)O)ORSherb
61188308carthamidinC1C(OC2=C(C1=O)C(=C(C(=C2)O)O)O)C3=CC=C(C=C3)OGJherb
62439533dihydroquercetinC1=CC(=C(C=C1C2C(C(=O)C3=C(C=C(C=C3O2)O)O)O)O)ORSherb
63441562DianthramineC1=CC(=C(C=C1O)NC2=C(C=CC(=C2)O)C(=O)O)C(=O)ORSherb
64441921ginsenoside reCC1C(C(C(C(O1)OC2C(C(C(OC2OC3CC4(C(CC(C5C4(CCC5C(C)(CCC=C(C)C)OC6C(C(C(C(O6)CO)O)O)O)C)O)C7(C3C(C(CC7)O)(C)C)C)C)CO)O)O)O)O)ORSPMID: 27080948
65441922ginsenoside rfCC(=CCCC(C)(C1CCC2(C1C(CC3C2(CC(C4C3(CCC(C4(C)C)O)C)OC5C(C(C(C(O5)CO)O)O)OC6C(C(C(C(O6)CO)O)O)O)C)O)C)O)CRSPMID: 29719465
66441923ginsenoside-Rg1CC(=CCCC(C)(C1CCC2(C1C(CC3C2(CC(C4C3(CCC(C4(C)C)O)C)OC5C(C(C(C(O5)CO)O)O)O)C)O)C)OC6C(C(C(C(O6)CO)O)O)O)CRSPMID: 18403247
67441965Frutinone AC1=CC=C2C(=C1)C3=C(C(=O)C4=CC=CC=C4O3)C(=O)O2RSherb
68442576PandamineCCC(C)C(C(=O)NC1C(OC2=CC=C(C=C2)C(CNC(=O)C(NC1=O)CC3=CC=CC=C3)O)C(C)C)N(C)CRSherb
69442793[6]-gingerolCCCCCC(CC(=O)CCC1=CC(=C(C=C1)O)OC)OGJherb
70442827TrifolirhizinC1C2C(C3=C(O1)C=C(C=C3)OC4C(C(C(C(O4)CO)O)O)O)OC5=CC6=C(C=C25)OCO6RSherb
71442847CelabenzineC1CCN(CCCNC(=O)CC(NC1)C2=CC=CC=C2)C(=O)C3=CC=CC=C3RSherb
72445154resveratrolC1=CC(=CC=C1C=CC2=CC(=CC(=C2)O)O)ORSherb
73485077DihydrochelerythrineCN1CC2=C(C=CC(=C2OC)OC)C3=C1C4=CC5=C(C=C4C=C3)OCO5HJherb
745503614-(1,5-Dimethylhex-4-enyl)cyclohex-2-enoneCC(CCC=C(C)C)C1CCC(=O)C=C1GJherb
75591309ShyobunoneCC(C)C1CCC(C(C1=O)C(=C)C)(C)C=CGJherb
76853433isoeugenolCC=CC1=CC(=C(C=C1)O)OCHJherb
771548943capsaicinCC(C)C=CCCCCC(=O)NCC1=CC(=C(C=C1)O)OCHJherb
781549025neryl acetateCC(=CCCC(=CCOC(=O)C)C)CGJ; HJherb
791549107(Z,Z)-farnesolCC(=CCCC(=CCCC(=CCO)C)C)CGJherb
803082861ginsenolCC1(CC2(C3(CCCC2(C1CC3)C)C)O)CRSherb
813084331T-MuurololCC1=CC2C(CCC(C2CC1)(C)O)C(C)CHJherb
825018391neocnidilideCCCCC1C2CCCC=C2C(=O)O1RSherb
8352757258-GingerolCCCCCCCC(CC(=O)CCC1=CC(=C(C=C1)O)OC)OGJherb
845280343quercetinC1=CC(=C(C=C1C2=C(C(=O)C3=C(C=C(C=C3O2)O)O)O)O)OGJ; HJherb
855280863kaempferolC1=CC(=CC=C1C2=C(C(=O)C3=C(C=C(C=C3O2)O)O)O)ORS; GJherb
865281147dehydrosafynolCC=CC#CC#CC#CC#CC(CO)OGJherb
875281153MycosinolCC#CC#CC=C1C=CC2(O1)C(C=CO2)ORSherb
885281441EnhydrinCC1C(O1)(C)C(=O)OC2C3C(C4C(O4)(CCC=C(C2OC(=O)C)C(=O)OC)C)OC(=O)C3=CRSherb
895281612DiosmetinCOC1=C(C=C(C=C1)C2=CC(=O)C3=C(C=C(C=C3O2)O)O)OHJherb
905281698SexangularetinCOC1=C(C=C(C2=C1OC(=C(C2=O)O)C3=CC=C(C=C3)O)O)OGJherb
915281775Gingerenone ACOC1=C(C=CC(=C1)CCC=CC(=O)CCC2=CC(=C(C=C2)O)OC)OGJherb
9252817946-shogaolCCCCCC=CC(=O)CCC1=CC(=C(C=C1)O)OCGJherb
935281846haplopineCOC1=C2C=COC2=NC3=C1C=CC(=C3OC)OHJherb
945315422zanthobungeanineCC1(C=CC2=C(O1)C3=C(C(=CC=C3)OC)N(C2=O)C)CHJherb
955315426zanthosimulineCC(=CCCC1(C=CC2=C(O1)C3=CC=CC=C3N(C2=O)C)C)CHJherb
965315645CampherenolCC(=CCCC1(C2CCC1(C(C2)O)C)C)CGJherb
9753167947,6′-dihydroxy-3′-methoxyisoflavoneCOC1=CC(=C(C=C1)O)C2=COC3=C(C2=O)C=CC(=C3)ORSherb
9853168912,5-dimethyl-7-hydroxy chromoneCC1=CC(=CC2=C1C(=O)C=C(O2)C)ORSherb
995317152(+)-1,5-Epoxy-nor-ketoguaia-11-eneCC1CCC23C1(O2)CC(CCC3=O)C(=C)CGJherb
1005317247Ethyl geranateCCOC(=O)C=C(C)CCC=C(C)CHJherb
1015317270zingiberolCC12CCCC(=C)C1CC(CC2)C(C)(C)OGJherb
1025317284NepetinCOC1=C(C2=C(C=C1O)OC(=CC2=O)C3=CC(=C(C=C3)O)O)ORSherb
10353175876-gingediacetateCCCCCC(CC(CCC1=CC(=C(C=C1)O)OC)OC(=O)C)OC(=O)CGJherb
1045317592Gingerenone BCOC1=CC(=CC(=C1O)OC)CCC=CC(=O)CCC2=CC(=C(C=C2)O)OCGJherb
1055317593gingerenone cCOC1=C(C=CC(=C1)CCC(=O)C=CCCC2=CC=C(C=C2)O)OGJherb
1065317596[4]-gingerolCCCC(CC(=O)CCC1=CC(=C(C=C1)O)OC)OGJherb
1075317632Ginsenoyne AC=CCCCCCC1C(O1)CC#CC#CC(C=C)ORSherb
1085317633Ginsenoyne BC=CCCCCCC(C(CC#CC#CC(C=C)O)O)ClRSherb
1095317634Ginsenoyne CC=CCCCCCC(C(CC#CC#CC(C=C)O)O)ORSherb
1105317635Ginsenoyne DCCCCCCCC1C(O1)CC#CC#CC(CC)ORSherb
1115318015HeptaphyllineCC(=CCC1=C(C(=CC2=C1NC3=CC=CC=C32)C=O)O)CGJherb
1125318039hexahydrocurcuminCOC1=C(C=CC(=C1)CCC(CC(=O)CCC2=CC(=C(C=C2)O)OC)O)OGJherb
1135318568isogingerenone bCOC1=CC(=CC(=C1O)OC)CCC(=O)C=CCCC2=CC(=C(C=C2)O)OCGJherb
1145319581AposiopolamineC=C(C1=CC=CC=C1)C(=O)OC2CC3C4C(O4)C(C2)N3RSherb
11553196918-methyl-5-isopropyl-6,8-nonadiene-2-oneCC(C)C(CCC(=O)C)C=CC(=C)CHJherb
1165320128cis-nerolidolCC(=CCCC(=CCCC(C)(C=C)O)C)CHJherb
117532013812-O-NicotinoylisolineoloneCC(=O)C1CCC2(C1(C(CC3C2(CC=C4C3(CCC(C4)O)C)O)OC(=O)C5=CN=CC=C5)C)ORSherb
11853201932,6-Nonamethylene pyridineC1CCCCC2=NC(=CC=C2)CCCC1GJherb
1195320290Onjixanthone ICOC1=C(C(=C2C(=C1)OC3=C(C2=O)C=C(C=C3)O)OC)OCGJherb
1205320291onjixanthone iiCOC1=C(C=C2C(=C1)C(=O)C3=C(O2)C=C(C(=C3O)OC)O)OGJherb
1215320336Ginsenoyne ECCCCCCCC1C(O1)CC#CC#CC(=O)C=CRSherb
1225320886Ramalic acidCC1=CC(=CC(=C1C(=O)O)O)OC(=O)C2=C(C(=C(C=C2C)OC)C)ORSherb
1235351594ChelerythrineC[N+]1=C2C(=C3C=CC(=C(C3=C1)OC)OC)C=CC4=CC5=C(C=C42)OCO5.[OH-]HJherb
12453524512,6-dimethyl-3,7-octadiene-2,6-diolCC(C)(C=CCC(C)(C=C)O)ORSherb
1255356544PeruviolCC(=CCCC(=CCCC(C)(C=C)O)C)CGJherb
1265365982Neryl propionateCCC(=O)OCC=C(C)CCC=C(C)CGJherb
1275469789panaxynolCCCCCCCC=CCC#CC#CC(C=C)ORSherb
12857483533-[[(2S)-2,4-dihydroxy-3,3-dimethylbutanoyl]amino]propanoic acidCC(C)(CO)C(C(=O)NCCC(=O)O)ORSherb
1296427501(E)-linalool oxide acetate pyrCC(=O)OC1CCC(OC1(C)C)(C)C=CHJherb
1306428574cis-linalol pyranoxideCC1(C(CCC(O1)(C)C=C)O)CHJherb
1316440935sanshoolCC=CC=CC=CCCC=CC(=O)NCC(C)CHJherb
1326442707SafynolCC=CC#CC#CC#CC=CC(CO)OGJherb
13364513376,8-Nonadien-2-one, 8-methyl-5-(1-methylethyl)-, (S-(E))-CC(C)C(CCC(=O)C)C=CC(=C)CHJherb
1346857681β-santalolCC(=CCCC1(C2CCC(C2)C1=C)C)CORSherb
1356917976ginsenoside rb2CC(=CCCC(C)(C1CCC2(C1C(CC3C2(CCC4C3(CCC(C4(C)C)OC5C(C(C(C(O5)CO)O)O)OC6C(C(C(C(O6)CO)O)O)O)C)C)O)C)OC7C(C(C(C(O7)COC8C(C(C(CO8)O)O)O)O)O)O)CRSPMID: 27977871
1366999975[(3S)-3,7-dimethyloct-6-enyl] acetateCC(CCC=C(C)C)CCOC(=O)CGJ; HJherb
1379898279ginsenoside rb1CC(=CCCC(C)(C1CCC2(C1C(CC3C2(CCC4C3(CCC(C4(C)C)OC5C(C(C(C(O5)CO)O)O)OC6C(C(C(C(O6)CO)O)O)O)C)C)O)C)OC7C(C(C(C(O7)COC8C(C(C(C(O8)CO)O)O)O)O)O)O)CRSPMID: 27601384
13810398656α-cadinolCC1=CC2C(CCC(C2CC1)(C)O)C(C)CRSherb
13910730081panaxydolCCCCCCCC1C(O1)CC#CC#CC(C=C)ORSherb
14010955174patchouli alcoholCC1CCC2(C(C3CCC2(C1C3)C)(C)C)ORSherb
141111164922-[(2S,5R)-5-ethenyl-5-methyloxolan-2-yl]propan-2-olCC1(CCC(O1)C(C)(C)O)C=CGJherb
14211241545ZINC02040970CC(=CCCC(=CCCC(C)(C=C)O)C)CGJ; HJherb
143114696491-alpha-Terpinyl acetateCC1=CCC(CC1)C(C)(C)OC(=O)CHJherb
14411877394neointermedeolCC(=C)C1CCC2(CCCC(C2C1)(C)O)CRSherb
14512085452(+)-MaalioxideCC1(C2CCCC3(C2C(O1)(CCC3)C)C)CRSherb
14612315453isocnidilideCCCCC1C2CCCC=C2C(=O)O1RSherb
14712806687SchinifolineCC(C)(C)C(C(CCCOC1=CC=CC=C1)N2C=NC=N2)OHJherb
14812855925ginsenoside rdCC(=CCCC(C)(C1CCC2(C1C(CC3C2(CC(C4C3(CCC(C4(C)C)O)C)OC5C(C(C(C(O5)CO)O)O)OC6C(C(C(C(O6)CO)O)O)O)C)O)C)OC7C(C(C(C(O7)CO)O)O)O)CRSPMID: 27503022
14912912363ginsenoside Rb3CC(=CCCC(C)(C1CCC2(C1C(CC3C2(CCC4C3(CCC(C4(C)C)OC5C(C(C(C(O5)CO)O)O)OC6C(C(C(C(O6)CO)O)O)O)C)C)O)C)OC7C(C(C(C(O7)COC8C(C(C(CO8)O)O)O)O)O)O)CRSPMID: 20662827
15013844273Gomisin BCC=C(C)C(=O)OC1C2=CC(=C(C(=C2C3=C(C4=C(C=C3CC(C1(C)O)C)OCO4)OC)OC)OC)OCRSherb
15114038843(1R,4E,7E,11R)-1,5,9,9-tetramethyl-12-oxabicyclo[9.1.0]dodeca-4,7-dieneCC1=CCCC2(C(O2)CC(C=CC1)(C)C)CRSherb
15214081290ginsenoside rh2CC(=CCCC(C)(C1CCC2(C1C(CC3C2(CCC4C3(CCC(C4(C)C)OC5C(C(C(C(O5)CO)O)O)O)C)C)O)C)O)CRSPMID: 32702586
15314135318bungeanoolCCC=CCC=CCCC=CC=CC(=O)NCC(C)(C)OHJherb
154151188165 ξ-hydroxy-1-(4-hydroxy-3-methoxyphenyl)-7-(4-hydroxyphenyl)-3-heptanoneCOC1=C(C=CC(=C1)CCC(=O)CC(CCC2=CC=C(C=C2)O)O)OGJherb
15515608605gomisin aCC1CC2=CC3=C(C(=C2C4=C(C(=C(C=C4CC1(C)O)OC)OC)OC)OC)OCO3RSherb
15616757678EstriolCC12CCC3C(C1CC(C2O)O)CCC4=C3C=CC(=C4)OHJherb
15721599923ginsenoside-Rh1CC(=CCCC(C)(C1CCC2(C1C(CC3C2(CC(C4C3(CCC(C4(C)C)O)C)OC5C(C(C(C(O5)CO)O)O)O)C)O)C)O)CRSPMID: 32695207
15823616650[(3S)-3,7-dimethyloct-7-enyl] acetateCC(CCCC(=C)C)CCOC(=O)CGJherb
15923616651[(3R)-3,7-dimethyloct-6-enyl] butanoateCCCC(=O)OCCC(C)CCC=C(C)CGJherb
16024832062geranyl acetateCC(=CCCC(=COC(=O)C)C)CGJ; HJherb
16124832102alpha-santalolCC(=CCCC1(C2CC3C1(C3C2)C)C)CORSherb
16244181925ginsenoside rcCC(=CCCC(C)(C1CCC2(C1C(CC3C2CCC4C3(CCC(C4(C)C)OC5C(C(C(C(O5)CO)O)O)OC6C(C(C(C(O6)CO)O)O)O)C)O)C)OC7C(C(C(C(O7)COC8C(C(C(O8)CO)O)O)O)O)O)CRSPMID: 23411022
16356840949DeoxyharringtonineCC(C)CCC(CC(=O)OC)(C(=O)OC1C2C3=CC4=C(C=C3CCN5C2(CCC5)C=C1OC)OCO4)ORSherb
16490473155malkanguninCC1CCC(C2(C13CC(C(C2OC(=O)C4=CC=CC=C4)OC(=O)C)C(O3)(C)C)CO)ORSherb
16597032059spathulenolCC1(C2C1C3C(CCC3(C)O)C(=C)CC2)CHJherb
16698104494aposcopolamineCN1C2CC(CC1C3C2O3)OC(=O)C(=C)C4=CC=CC=C4RSherb
167101603339sagittariolCC1CCC2(C(C1(C)CCC(C)(C=C)O)CCC=C2CO)CGJherb
1681016602756-gingediolCCCCCC(CC(CCC1=CC(=C(C=C1)O)OC)O)OGJherb
169118701072tauremisinCC1C2CCC3(C(C2OC1=O)C(C=CC3=O)(C)O)CRSherb
170129716080(9r,10s)-epoxyheptadecan-4,6-diyn-3-oneCCCCCCCCCCC#CC#CC(=O)CC=ORSherb
171132350840suchilactoneCOC1=C(C=C(C=C1)CC2COC(=O)C2=CC3=CC4=C(C=C3)OCO4)OCRSherb
172132587053humulene epoxide iCC1=CCC(C=CCC2(C(O2)CC1)C)(C)CRSherb
173139600351Ginsenoside Rg3CC(=CCCC(C)(C1CCC2(C1C(CC3C2(CCC4C3(CCC(C4(C)C)OC5C(C(C(C(O5)CO)O)O)OC6C(C(C(C(O6)CO)O)O)O)C)C)O)C)O)CRSPMID: 26199555
Table
Table 2. Docking results of hub genes and compounds from Dajianzhong decoction.
Table 2. Docking results of hub genes and compounds from Dajianzhong decoction.
Pubchem IDCompound NameHerbTarget NameUniprot IDPDB IDAffinity
(kcal/mol)		Interaction
Hydrogens BondHydrophobic Interactionsπ-Cation Interactions
PL 5I6X−10.8
16757678EstriolHJSLC6A4P316455I6X−10.3335A95A, 172A, 176A, 341A
161298InerminRSSLC6A4P316455I6X−10.295A, 497A172A, 176A, 335A, 341A, 501A
68486SuberosinHJSLC6A4P316455I6X−9.7177A, 439A95A, 172A, 173A, 176A, 341A, 501A
4970FumarineRSSLC6A4P316455I6X−8.395A, 334A169A, 172A, 176A, 334A, 335A, 341A, 501A
93135XanthorrhizolGJSLC6A4P316455I6X−8.3169A95A, 172A, 173A, 176A, 334A, 341A, 501A
23535Linalyl anthranilateHJSLC6A4P316455I6X−7.6176A, 335A95A, 172A, 341A, 501A
10748AyapaninHJSLC6A4P316455I6X−7.5177A95A, 173A, 341A
442793[6]-gingerolGJSLC6A4P316455I6X−7.5169A, 177A, 438A, 439A95A, 172A, 173A, 176A, 335A, 341A, 443A, 501A
92138ElemolGJSLC6A4P316455I6X−7.3-176A, 335A, 341A, 501A
3026DBPRS; GJSLC6A4P316455I6X−7.295A, 176A95A, 341A
6427501PyranoidHJSLC6A4P316455I6X−6.5335A172A, 176A, 334A, 341A, 501A
PL 6PYA−10.2
16757678EstriolHJSHBGP042786PYA−8.342A, 82A, 105A, 127A67A, 105A, 112A, 171A
101603339SagittariolGJSHBGP042786PYA−7.342A, 127A, 135A67A, 105A, 107A, 112A
PL 7A69−9.3
5280343QuercetinGJ; HJABCB1P081837A69−6.8344A, 347A, 871A, 946A875A
68077TangeretinHJABCB1P081837A69−6.5310A, 344A, 725A65A, 340A, 343A, 728A, 983A
5280863KaempferolRS; GJ; HJABCB1P081837A69−6.4310A303A, 306A, 339A, 343A
445154ResveratrolRSABCB1P081837A69−6.1232A, 310A232A, 303A, 306A, 343A
PL 3E7G−6.9
139600351Ginsenoside Rg3RSNOS2P352283E7G−8.5201A, 350A, 371A, 372A, 374A, 377A
485077DihydrochelerythrineHJNOS2P352283E7G−7.8199A, 491A197A, 350A, 352A, 373A
2353BerberineHJNOS2P352283E7G−7.6263A, 266A350A
5280343QuercetinGJ; HJNOS2P352283E7G−7.2351A, 377A, 385A, 387A, 388A350A
5280863KaempferolRS; GJNOS2P352283E7G−7.2351A, 373A, 377A, 387A, 388A350A
5281612DiosmetinHJNOS2P352283E7G−7.2351A, 372A352A
439533TaxifolinRSNOS2P352283E7G−7.1387A, 388A381A388A
68077TangeretinHJNOS2P352283E7G−6.7-263A
441921Ginsenoside reRSNOS2P352283E7G−6.7201A, 202A, 381A, 388A, 491A199A, 463A
445154ResveratrolRSNOS2P352283E7G−6.7352A, 371A, 388A350A, 373A
5318039HexahydrocurcuminGJNOS2P352283E7G−6.6263A, 350A, 352A, 369A371A
3314EugenolGJ; HJNOS2P352283E7G−4.9266A, 382A, 388A263A, 350A, 373A, 381A
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Zhao, Y.; Wang, S.; Hu, Y.; Wang, Y. Investigating Key Targets of Dajianzhong Decoction for Treating Crohn’s Disease Using Weighted Gene Co-Expression Network. Processes 2023, 11, 112. https://doi.org/10.3390/pr11010112

AMA Style

Zhao Y, Wang S, Hu Y, Wang Y. Investigating Key Targets of Dajianzhong Decoction for Treating Crohn’s Disease Using Weighted Gene Co-Expression Network. Processes. 2023; 11(1):112. https://doi.org/10.3390/pr11010112

Chicago/Turabian Style

Zhao, Yi, Shengpeng Wang, Yuanjia Hu, and Yitao Wang. 2023. "Investigating Key Targets of Dajianzhong Decoction for Treating Crohn’s Disease Using Weighted Gene Co-Expression Network" Processes 11, no. 1: 112. https://doi.org/10.3390/pr11010112

APA Style

Zhao, Y., Wang, S., Hu, Y., & Wang, Y. (2023). Investigating Key Targets of Dajianzhong Decoction for Treating Crohn’s Disease Using Weighted Gene Co-Expression Network. Processes, 11(1), 112. https://doi.org/10.3390/pr11010112

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