Combining network pharmacology and bioinformatics analysis to identify the molecular mechanisms of UDCA in the treatment of carotid atherosclerosis

doi:10.21203/rs.3.rs-1606696/v1

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Combining network pharmacology and bioinformatics analysis to identify the molecular mechanisms of UDCA in the treatment of carotid atherosclerosis

https://doi.org/10.21203/rs.3.rs-1606696/v1

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Ursodeoxycholic acid (UDCA) can be used to prevent atherosclerosis. However, the molecular mechanism of UDCA on carotid atherosclerosis is not known. A combination of bioinformatics, network pharmacology, and molecular docking was used to unravel the potential molecular mechanisms of UDCA in alleviating carotid atherosclerosis. The results of weighted gene co-expression network analysis (WGCNA) combined with differential expression analysis were used to screen for potent targets in carotid atherosclerosis. A typical target for UDCA in the treatment of carotid atherosclerosis was obtained by network pharmacology, and a protein-protein interaction network was constructed. Nine core targets were obtained using four algorithms of the cytoHubba plugin of Cytoscape 3.9.0, and molecular docking was completed using Autodock Vina 1.1.2. GO, and KEGG enrichment pathway analysis was performed based on the Metascape platform. The results showed that CD4, PTPRC, CCL5, MMP9, CD38, CASP1, CCR2, EGFR, and SYK were the targets of action of UDCA in the treatment of carotid atherosclerosis. The enrichment analysis results showed that these targets were significantly enriched in pathways associated with inflammation. In conclusion, this study highlights some of the molecular mechanisms of UDCA in the treatment of carotid atherosclerosis and provides a theoretical basis for future studies.

Atherosclerosis is a multifactorial, chronic inflammatory disease that causes acute ischemic stroke and myocardial infarction (MI)¹. Atherosclerosis is characterized by increased activation of inflammatory mechanisms and their bidirectional connection with lipid disease and buildup. Endothelial cell damage, lipid buildup, migration of monocyte-derived macrophages, growth factor release, and activation of vascular smooth muscle cells all contribute to atherosclerotic plaques (VSMC)². All phases of the atherosclerotic process involve inflammatory cytokines and chemokines³. Where Inflammation plays a central role in the formation of atherosclerotic plaques and the development of AS⁴. Changes in the internal carotid artery (ICA) can create plaque vulnerability, leading to local platelet activation and thrombosis, the primary cause of carotid-associated cerebrovascular ischemic episodes⁵.

UDCA is a naturally occurring hydrophilic tertiary bile salt that has long been utilized to treat cholestatic liver illnesses such as primary biliary cirrhosis and chronic viral hepatitis^6,7. UDCA has cytoprotective properties, including ephedrine and anti-apoptotic actions. However, these properties have only been documented in hepatocytes. UDCA has been linked to anti-atherosclerosis activity, according to some studies. By decreasing ER stress and the inflammatory response, UDCA exerts an anti-atherosclerotic effect in atherosclerosis caused by disrupted blood flow⁸. UDCA impaired CC and NLRP3-dependent inflammation and reduced atherosclerosis in mice by increasing cholesterol solubility⁹. UDCA decreases endothelial dysfunction (by reducing RAGE signaling) and macrophage foam cell production, inhibiting diabetic atherosclerotic lesions¹⁰. In human vascular EC, UDCA promotes nitric oxide generation while inhibiting endothelin-1 synthesis¹¹. Taurine conjugated ursodeoxycholic acid (TUDCA) inhibits neointimal hyperplasia by reducing proliferation and inducing apoptosis of vascular smooth muscle cells in rats with carotid balloon injury¹². These findings show that UDCA may perform a cytoprotective role in atherosclerosis.

In recent years, advances in computer technology have enabled us to use network pharmacology to systematically analyze the effect of medications on illnesses, considerably expanding our understanding of the molecular network of pharmaceuticals and their attributes. To further illustrate the comprehensive mechanism of UDCA for the treatment of carotid atherosclerosis, we combined a network pharmacology approach with a gene expression master library (GEO) to explore potential pathways and screen for molecular targets. First, DEGs of carotid atherosclerosis were obtained by GSE100927 and GSE43292 datasets, and gene co-expression modules associated with carotid atherosclerosis were identified from the two datasets using WGCNA. The regulatory networks of UDCA and target genes were constructed by the National Library of Medicine and online tools. UDCA intersects the target genes with 63 genes selected as critical genes. We then investigated the functional and pathway enrichment of putative targets and overlapping central genes using a comprehensive bioinformatics approach. Furthermore, the degree of interaction between the core gene and UDCA was validated using a molecular docking technique. By combining network pharmacology and bioinformatics, we identified the core targets and pathways that contribute to the pathogenesis of UDCA against carotid atherosclerosis. We screened the molecular targets for their potential involvement. In Fig. 1, the workflow is described.

Data Preprocessing

The transcriptional profiles downloaded from the GEO database were preprocessed. Principal component analysis (PCA) plots and Boxplots after normalization using the R package limma are shown in Fig. 2. Normalized data are provided in Supplementary Table S1, S2.

Co-expression network construction and module identification

To find the functional clusters of HNSCC patients, a coexpression network with no quantity scale was constructed with the WGCNA software package for the preprocessed GSE10092 dataset and GSE43292 dataset, respectively. The soft threshold power β is 3, and the scale-free R2 > 0.85 (Supplementary Fig S3, S4). By assigning a color to each module, a total of 53 modules in GSE100927 (Fig. 3B) and nine modules in GSE43292 (Fig. 4B) were identified in this study (excluding the gray modules). Heat maps of module-trait relationships were also plotted to evaluate the correlation between each module and two clinical traits (carotid atherosclerosis and normal). Genes in the gray module were not correlated and were excluded from subsequent analyses. We plotted the scatter plot of gene significance (GS) versus module membership (MM). In the GSE100927 dataset, the turquoise module was identified as having the best correlation with carotid atherosclerosis (turquoise module: r = 0.91, p = 1e-200) (Fig. 3D) (Supplementary Table S5), the greenyellow module had the highest correlation with normal tissue (greenyellow module: r = 0.79, p = 2e-100) (Fig. 3E) (Supplementary Table S6). In the GSE43292 dataset, the Brown Module had the highest relationship with carotid atherosclerosis (brown module: r = 0.77, p = 1e-200) (Fig. 4E) (Supplementary Table S7), and the turquoise module had the highest correlation with normal tissue (turquoise module: r = 0.65, p = 1e-200) (Fig. 4D) (Supplementary Table S8).

Identification of differentially expressed genes

A total of 8146 DEGs were found in the GSE100927 dataset (Fig. 5A) and the GSE43292 dataset (Fig. 5B) using the limma package with the screening criteria of |logFC| ≥ 0.5 and false discovery rate adjusted p-value (FDR) < 0.05 (Supplementary Table S9, S10). 7699 and 7100 co-expressed genes were found in the GSE100927 dataset and GSE43292 dataset, respectively, which were the most clinically relevant modules. In total, 903 overlapping genes were extracted (Fig. 5C).

Construction of UDCA and target gene network

The 2D structure of UDCA was downloaded from the PubChem website, shown in Fig. 6C. A total of 551 genes (Supplementary Table S11) were identified as targets of UDCA from the SwissTargetPrediction database, SuperPred database, Similarity approach ensemble database, and PharmMapper database. Combining drug targets with disease targets resulted in 63 core targets (Supplementary Table S12) plotting the Venn diagram online (Fig. 6A). We used the STRING database to create a PPI network of overlapping genes, resulting in 57 interacting proteins (Fig. 6B). Then, we constructed the network between UDCA and target genes by Cytoscape 3.9.0 (Fig. 6D).

Identification of hub genes

The results for obtaining protein interaction relationships were exported from the String database in tag separation value (TSV) format (Supplementary Table S13). The PPI network of 57 genes was calculated using the Maximal Clique Centrality (MCC), Maximum neighborhood component (MNC), Degree, and Edge Percolated component (EPC) algorithms of the cytoHubba plugin of Cytoscape 3.9.0, and the top 10 genes were chosen as hub genes. The PPI network maps of the genes discovered by these four algorithms are shown in Fig. 7.

These genes are shown in Supplementary Table S14. A Venn diagram was constructed using the intersection of the hub genes obtained by these algorithms (Fig. 8A). Finally, nine genes were acquired, including CD4, PTPRC, CCL5, MMP9, CD38, CASP1, CCR2, EGFR, and SYK. In addition, we compared the differences in expression of 9 central genes in carotid atherosclerosis versus standard samples in both data sets (Figs. 8B, C). There were significant gene expression differences between carotid atherosclerosis samples and normal samples.

Functional Enrichment Analysis of Hub Genes

Nine central genes were subjected to functional enrichment analysis to grasp more about carotid atherosclerosis and UDCA from a biological standpoint. KEGG results showed that it was mainly involved in the interaction signaling pathways of lipid-arteriosclerosis, coronavirus disease-COVID-19, Hirschsprung's disease, and cytokine-cytokine receptors (Fig. 9A). GO analysis showed that carotid atherosclerosis was significantly associated with several biological processes, including positive regulation of lymphocyte proliferation, regulation of inflammatory response, positive regulation of peptidyl tyrosine phosphorylation regulation of α-β T cell proliferation, regulation of cytokine-mediated signaling pathways, and transmembrane receptor protein tyrosine kinase signaling pathways (Fig. 9B). Molecular function (MF) analysis showed that UDCA was significantly associated with kinase binding, cytokine receptor binding, protein kinase activity, protein kinase binding, phosphotransferase activity, alcohol group receptor, and kinase activity (Fig. 9C). Cellular component (CC) analysis showed that OGEs were mainly enriched on one side of the membrane, membrane rafts, membrane microdomains, inner cell vesicles, lateral serosa, receptor complexes, and plasma membrane protein complex regions (Fig. 9D). Details of GO and KEGG analyses are provided in Supplementary Table S15.

Molecular docking

AutoDock Vina performed these nine potential target proteins and their docking with UDCA molecules. The docking results indicated that the bound affinity was not greater than − 6.0 kcal/mol, demonstrating that the CKI fraction had an excellent binding capacity to the target (Table 1). UDCA diminishes the progression of carotid atherosclerosis by modulating targets such as MMP9, CCL5, CD4, CCR2, EGFR, CASP1, PTPRC, SYK, and CD38, which is in line with the results of the network pharmacology (NP) screen. Figure 10 shows the findings of docking among UDCA ligands and aim protein receptors.

Table 1

Molecular docking scores of UDCA to target proteins (kcal/mol).
Receptor ligand	MMP9	CCL5	CD4	CCR2	EGFR	CASP1	PTPRC	SYK	CD38
UDCA	− 7.7	− 6.4	− 7.9	− 8.2	− 8.6	− 6.9	− 7	− 8.2	− 8.3

Atherosclerosis has long been recognized as an obey-driven disease, and thus therapeutic strategies have focused on lipid-lowering approaches. However, numerous studies have shown that inflammation has an essential role in the atherosclerotic process. The inflammatory nature of atherosclerosis implies that many potential targets can modulate atherosclerosis. Because atherosclerosis is a complicated, multitargeted, inflammatory-driven, chronic disease, its treatment relies on permanent drug administration. Therefore, there is a need to develop anti-inflammatory drugs with more novel, safe, and multi-targets¹³. Previously, ursodeoxycholic acid was used to prevent atherosclerosis in patients with nonalcoholic fatty liver disease¹⁴. Some studies have found that UDCA may exert anti-atherosclerosis activity by inhibiting intimal hyperplasia through resisting inflammation^8–12. In our study, a comprehensive analysis was performed using a network pharmacology approach combined with gene expression profiling to identify further UDCA's potential mechanisms and therapeutic targets in carotid atherosclerosis.

Nine key target genes were identified by bioinformatics analysis as potential anti-HCC mechanisms by which UDCA antagonizes carotid atherosclerosis. Nine targets, including MMP9, CCL5, CD4, CCR2, CASP1, EGFR, PTPRC, SYK, and CD38, were significantly expressed in atherosclerotic plaques vascular tissues compared with normal controls. Nine central genes are broadly related to the positive regulation of lymphocyte proliferation, inflammatory response regulation, cytokine-mediated signaling pathways, transmembrane receptor protein tyrosine kinase signaling, and other biological processes. KEGG enrichment results showed that UDCA acted on carotid atherosclerosis by acting on core genes such as MMP9, CCL5, CD4, CCR2, and CASP1 enriched in the lipid and arteriosclerosis pathways, and cytokine-cytokine receptor interaction signaling pathways. Among them, the effect on CD4 is reflected in the non-canonical pathway. Most studies have demonstrated that chronic inflammation contributes centrally to the evolution and progression of atherosclerosis, which involves activating many inflammatory cytokines and chemokines. Inflammation can contribute to the transdifferentiation of vascular smooth muscle cells VSMC into macrophage-like cells¹⁵. MMP-9, produced by infiltrating macrophages and vascular smooth muscle cells VSMC, induces migration and proliferation of monocytes and smooth muscle cells (SMC)^16–21 and affects overall plaque stability by promoting degradation of the extracellular matrix and NF-κB signaling pathways ^22–25 thereby promoting fibrous plaque cap thinning^26,27. MMP9 enhances the infiltrative density of inflammation, accelerates the damage of blood vessels, and modulates the movement of monocytes and T cells into the vessel wall²⁸. Among the central genes, PTPRC (also known as CD45) and MMP9 have been demonstrated as inflammatory biomarkers of atherosclerosis^29,30. Matrix metalloproteinase 9 (MMP-9) protein is predominantly found in the narrowest regions of human carotid plaques ³¹. Data mining of the human plaque transcriptome showed that CCL5 expression was higher in carotid arteries (23.3-fold increase) than in coronary plaques (2.9-fold increase)³². The chemokine CCL5 is secreted by activated platelets and is expressed in atherosclerotic plaques^33,34. CCL5 is deposited on endothelial cells through interaction with specific chemokine receptors, thereby mediating the migration of inflammatory cells such as T cells and monocytes to the intima and promoting atherosclerosis^35–37. In injured arteries, CCL5 (RANTES) is increased in Vessel wall cells and synergizes with leukocyte recruitment to damaged arteries during revascularization^38,39. In addition, high RANTES expression in perivascular adipose tissue plays a significant function in mediating vascular inflammatory cell recruitment in endothelial dysfunction^40,41. Relevant studies have demonstrated that activation and assembly of the inflammasome lead to self-cleavage and activation of caspase-1, which converts inactive pro-IL-1B and pro-IL-18 into active IL-1β and IL-18 by proteolysis⁴². To induce inflammation and promote the development of atherosclerosis^43–46. Epilepsy It has been shown that cholesterol within the arterial wall triggers NLRP3 inflammasome-mediated activation of caspase-1. In the following, the release of mature IL-1 and IL-18, and IFN- will promote the development of atherosclerosis by altering the recruitment of monocytes to the lesion site, altering the activation state of diseased macrophages and the ability of these cells to form foam cells, and altering the activation state of other immune cells known to promote atherosclerosis, such as natural killer cells, T lymphocytes, and natural killer T cells⁴⁷. Studies have confirmed that CCR2 is a useful marker of atherosclerosis⁴⁸. CCR2 is a high-affinity receptor for CCL2 and is expressed on human endothelial cells^49,50. The chemokine CCL2 mediates monocyte infiltration into atherosclerotic lesions through CC chemokine receptor 2 (CCR2)⁵¹ and plays a vital role in monocyte adhesion to the endothelium⁵². CCL2-CCR2 signaling through recruitment of inflammatory monocytes to atherosclerotic plaque sites⁵³. It can also impair endothelial HDL uptake and cholesterol efflux and become a risk factor for atherosclerosis⁵⁴.

In summary, UDCA may inhibit the development of inflammation mainly through inhibiting related proteins, chemokines, and chemokine receptors, thus playing a role in cervical atherosclerosis. In this study, we combined bioinformatics and network pharmacology approaches to elucidate the molecular mechanisms of UDCA in the treatment of carotid atherosclerosis. Hub genes involved in carotid atherosclerosis were searched for and identified using WGCNA and differential expression analysis. The regulatory network of drugs and hub genes was constructed, and the disease process in which the core genes are involved was further analyzed. The molecular docking method suggested that UDCA had a high binding affinity to the central target, validating the medicinal effect of UDCA. However, although the analysis results were able to identify a role involving UDCA in the treatment of carotid atherosclerosis, the data sources were limited to databases, and there was a lack of in vitro or in vivo experiments to further validate our results. In-depth basic and clinical studies and clinical studies are needed in future studies to elucidate the positive role of UDCA in slowing the progression of carotid atherosclerosis.

In conclusion, our study suggests that UDCA may act primarily on carotid atherosclerosis by influencing inflammatory processes. MMP9, CCL5, CASP1, CD4, and CCR2, may be critical potential target proteins of UDCA in treating carotid atherosclerosis. Additionally, molecular docking proved that the UDCA component docked well with the core protein, providing an essential basis for further study.

Data collection and pretreatment

GSE100927 and GSE43292 gene expression profiles were collected from the gene expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). Both gene expression profiles were obtained from human samples. The dataset GSE100927 was performed based on the platform GPL17077 Agilent-039494 SurePrint G3 Human GE v2 8 x 60K Microarray 039381 (Probe Name version), which included 29 carotid atherosclerotic tissue samples and 12 carotid tissue samples without atherosclerotic lesions⁵⁵. The GSE43292 series was performed by GPL6244 [HuGene-10-st] Affymetrix Human Gene 1.0 ST Array [transcript (gene) version] and included 32 carotid atherosclerotic plaque samples and 32 macroscopically intact carotid tissue samples⁵⁶. The R packages limma normalize between arrays function and reprocessing expression data for background correction and normalization.

Weighted gene co-expression network analysis (WGCNA)

WGCNA is an algorithm for clustering genes with similar expression patterns and building gene co-expression networks⁵⁷. It was used to identify common expression networks in R software. We used the R package WGCNA (version 1.70, Fig. https://cran.r-project.org/web/packages/WGCNA/) to explore modules linked with attributes from GSE43292 and GSE100927 independently⁵⁸. WGCNA was used to build gene co-expression networks using expression data corrected and normalized using the limma R program. First, the Pearson correlation coefficient was used to compute the distance between each gene and create a correlation matrix. The similarity matrix was transformed into an adjacency matrix using a power adjacency function^58,59. Use the integral function (pickSoftThresshold) to select the appropriate soft threshold power β and thereby improve the similarity of the co-expression and achieve a scale-free topology^59,60. Second, the genes were clustered into multiple modules using the mean linkage hierarchical clustering method. The minimum number of genes in the modules was set to 30, and the merging threshold was 0.25. Modules with similarities higher than 0.85 were fused. Third, Pearson correlation coefficients between modules and CAD were calculated. The modules with the highest correlation coefficient and p < 0.05 were selected and analyzed.

Differentially expressed gene identification

R package limma (linear model for microarray data) provides an integrated solution for differential expression analysis of RNA sequencing and microarray data⁶¹. The DEGs in the GSE100927 and GSE43292 datasets were filtered out using the limma algorithm. To control the false discovery rate (FDR), P-values were corrected using the Benjamini-Hochberg approach. A critical criterion gene with | logFC | ≥ 0.5 and an adjusted P < 0.05 were considered DEGs. The R package ggplot2 was used to visualize DEGs as volcano plots⁶².

Subsequently, potential prognostic genes were identified using overlapping genes between genes obtained from DEGs and co-expression networks, presented as Venn diagrams using the R package Venn.

Prediction of UDCA targets and construction of target gene networks

The chemical structure of UDCA was acquired from the PubChem website (https://pubchem.ncbi.nlm.nih.gov/ )⁶³. The target prediction of UDCA was performed using the SwissTargetPrediction database (http://www.swisstargetprediction.ch/ )⁶⁴, Metaplasia SEA database

( http://sea.bkslab.org )⁶³ and the PharmMapper database (http://lilab.ecust.edu.cn/pharmmapper/ )⁶⁴, while the species is limited to "Homo sapiens.” Due to the non-standard nomenclature, we used the UniProtKB database (http://www.uniprot.org/), and the corresponding official symbols were obtained. Finally, after removing duplicate targets, all putative targets of UDCA were retrieved.

We used the online instrument (STRING) 11.5 (https://string-db.org/)⁶⁴ to predict the interactions between target genes and then constructed regulatory networks of chemical compounds and candidate genes by Cytoscape 3.9.0 (https://www.cytoscape.org/).

Hub gene identification

We used the MCC, MNC, degree, and EPC algorithms of the cytoHubba plugin for Cytoscape 3.9.0 to calculate the PPI network. The intersection of the top 10 genes of the score was obtained using a Venn diagram as the pivot gene.6. functional enrichment analysis of hub genes.

Functional enrichment analysis of hub genes

The Metascape database (http://metascape.org/) was used for Gene Ontology (GO) and KEGG enrichment analyses⁶⁵. The items with P < 0.01, minimum count 3, and enrichment factor > 1.5(enrichment factor is the ratio between observed count and accidental expected count) were collected and grouped into clusters according to the similarity of their members. The Go chord diagram was created by the online platform (http://www.bioinformatics.cn).

Molecular docking

The binding energy of a drug molecule to a protein receptor can be derived by calculating the binding affinity between the ligand and the receptor and the interaction^66,67. The core target protein structures were downloaded from the Protein Data Bank (PDB) database (http://www.rcsb.org/), removing water molecules and isolating the original ligands using PyMol 1.8 software. Import the results into Autodock Tools 1.5.6 application and transform them into the "pdbqt" style. The UDCA structure in SDF format was downloaded from the PubChem website, then minimized and saved in mol2 format using Chem3D software (https://www.chemdraw.com.cn/biaoqian/Chem3D), and finally imported into Autodock Tools 1.5.7 (https://autodock.scripps.edu/) application for conversion to "pdbqt" format. The "pdbqt" format files were imported into Autodock-vina 1.1.2 (https://autodock.scripps.edu/) software for molecular docking, and the docking results were finally visualized with PyMol software (https://pymol.org/2/).

Data Availability

The datasets analyzed during the current study are available in the [Gene Expression Omnibus (GEO)] repository, [GSE100927, GSE43292].

The Macromolecular structure analyzed during the current study is available in the [Worldwide Protein Data Bank (wwPDB)] repository, [1G9M, 5T1A, 1YGR, 1M17, 1IBC, IL6J, 1XBC, 1B3A].

Data Availability

The datasets analyzed during the current study are available in the [Gene Expression Omnibus (GEO)] repository, [GSE100927, GSE43292].

The Macromolecular structure analyzed during the current study is available in the [Worldwide Protein Data Bank (wwPDB)] repository, [1G9M, 5T1A, 1YGR, 1M17, 1IBC, IL6J, 1XBC, 1B3A].

Acknowledgements

The study was funded by the Science and Technology Development Project Plan of Weifang City (No. 8192009, and 8192426).

Author contributions statement

X.X.K. and Z.Y.Q. conceived and designed the study. X.X.K. and L.X.J. collected and analyzed the data. X.X.K. wrote the manuscript. S.B.L. directed the research and suggested revisions to the manuscript. All authors reviewed and approved the final manuscript.

Competing interests

The authors declare no competing interests.

Additional information

Supplementary Information The data that support the findings of this study are available in the supplementary material of this article.

Correspondence and requests for materials should be addressed to S.B.L.

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No competing interests reported.

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Combining network pharmacology and bioinformatics analysis to identify the molecular mechanisms of UDCA in the treatment of carotid atherosclerosis

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Abstract

Figures

Introduction

Results

Data Preprocessing

Co-expression network construction and module identification

Identification of differentially expressed genes

Construction of UDCA and target gene network

Identification of hub genes

Functional Enrichment Analysis of Hub Genes

Molecular docking

Discussion

Methods

Data collection and pretreatment

Weighted gene co-expression network analysis (WGCNA)

Differentially expressed gene identification

Prediction of UDCA targets and construction of target gene networks

Hub gene identification

Functional enrichment analysis of hub genes

Molecular docking

Data Availability

Declarations

Acknowledgements

Author contributions statement

Competing interests

Additional information

References

Additional Declarations

Supplementary Files

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