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. 2026 Mar 9;11(11):17148–17161. doi: 10.1021/acsomega.5c06829

Gualou-Xiebai-Baijiu Decoction Ameliorates Atherosclerosis by Inhibiting the ARG1/l‑Arg/NO Pathway: A Study Based on Network Pharmacology

Yulin Chen †, Kai Hu †, Yingying He †, Runzhen Li †, Yanhui Meng †, Sha Nie †, Yaoping Tang †,‡,*
PMCID: PMC13019185  PMID: 41908443

Abstract

This study explored the potential mechanisms of action of Gualou-Xiebai-Baijiu Decoction (GXBD) in the treatment of atherosclerosis (AS) by integrating computational analyses with preliminary animal experiments. The putative targets of blood-absorbed components in GXBD were obtained and then intersected with AS-related targets, followed by protein–protein interaction network construction, core target identification, and GO and KEGG enrichment analyses. Targets presenting potential causal associations with AS were determined with Mendelian randomization (MR) analyses. Binding stability between candidate compounds and key targets was evaluated with molecular docking and molecular dynamics (MD) simulations. Finally, a mouse model of AS was established for in vivo validation. A total of 379 targets of six blood-absorbed components in GXBD and 1975 AS-related targets were identified, among which 154 were overlapping genes and 64 were further defined as core targets. Enrichment analysis results indicated the involvement of pathways including fluid shear stress, PI3K-Akt, and focal adhesion. Among the targets of GXBD, ARG1, CCR3, EPHB4, MAPK9, MCL1, and PDK1 showed significant causal associations with AS, with ARG1 determined as the key target. Molecular docking and MD simulations demonstrated that Chrysoeriol glucuronide might stably interact with ARG1. In ApoE–/– mice, GXBD treatment significantly decreased ARG1 expression in the aorta, increased serum l-Arg and NO levels, improved lipid profiles, and reduced aortic plaque burden. These findings provide preliminary evidence that GXBD may ameliorate AS-related pathological changes via the ARG1/l-Arg/NO pathway, offering mechanistic insights into the multicomponent actions of GXBD in AS treatment and highlighting further validation directions.

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1. Introduction

Atherosclerosis (AS), a complex chronic inflammatory disease, constitutes a leading cause of cardiovascular diseases. This disease is a process that involves lipid deposition in the arterial intima, inflammatory cell infiltration, smooth muscle cell proliferation and migration, and fibrous cap formation − and is influenced by various factors including genetics, high-fat diet, smoking, hypertension, and diabetes. − Currently, the treatment of AS mainly relies on pharmacotherapy, commonly including statins, antiplatelet agents, and antihypertensive drugs, and lifestyle interventions. , Unfortunately, pharmacotherapy not only contributes to poor treatment responses in some patients but also may produce certain side effects when used long-term. , Therefore, it is urgent to develop novel, safe, and effective therapeutic strategies for AS.

Traditional Chinese medicine (TCM) has a long history and rich experience in the prevention and treatment of cardiovascular diseases, with unique advantages because of its multitarget and multipathway characteristics. As a classic TCM prescription, Gualou-Xiebai-Baijiu Decoction (GXBD) is frequently utilized for the treatment of cardiovascular diseases. , Gualou mainly consists of flavonoids, saponins, and terpenoids, which possesses the effects of clearing heat, resolving phlegm, relieving chest congestion, and dispersing masses. Xiebai is known for promoting yang, dissipating stagnation, regulating Qi, and alleviating pain, which is primarily composed of volatile oils, saponins, and polysaccharides. − Modern pharmacological studies have unveiled the antioxidant, anti-inflammatory, and lipid-lowering effects of Gualou extracts and the antiplatelet aggregation, vasodilation, and blood circulation-improving activities of Xiebai extracts. − Nevertheless, it remains elusive about the specific active components and key molecular targets of GXBD involved in AS treatment, necessitating further investigation.

The Mendelian randomization (MR) analysis is an innovative and effective epidemiological approach that delves into the causal relationship between exposure and disease outcomes with genetic variants as unbiased instrumental variables, thereby diminishing confounding and reverse causation and enhancing the quality of causal inference. Medical research has increasingly used single-nucleotide polymorphisms (SNPs) as instrumental variables since their alleles are assigned to individuals before any exposure, ensuring the comparability of all known and unknown confounders and enabling the assessment of causal relationships between exposure and outcome.

Molecular dynamics (MD) simulations are a computational method that is widely harnessed to ascertain the interaction processes between compounds and proteins. By simulating the atomic trajectories of compound–protein systems under specific conditions (such as temperature and pH) over time, MD dynamically unravels the effects of compounds on protein structural stability, conformational changes, and the microenvironment of binding sites. Different from static molecular docking, MD simulations offer insights into the real-time dynamic responses of compound–protein systems, which is of great value in drug discovery, molecular recognition mechanism analyses, and candidate molecule screening.

Accordingly, this study analyzed the potential mechanisms of action of GXBD in AS by integrating network pharmacology, MR analyses, molecular docking, MD simulations, and preliminary animal experiments. By combining computational predictions with in vivo validation, we sought to provide mechanistic insights into the potential role of GXBD in AS and offer a research framework for investigating TCM formulas (Figure ).

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Workflow of the study.

2. Materials and Methods

2.1. Experimental Animals

A total of 33 male C57BL/6N Apoe–/– mice (specific pathogen-free [SPF] grade; 8 weeks old; 20–22 g) were obtained from Cyagen Biosciences (Taicang, China; license no. SCXK [Su] 2018-0003). All mice were housed in the Animal Center of Guangxi University of Chinese Medicine (license no. SYXK [Gui] 2024-0004) under SPF conditions (temperature, 25 ± 1 °C; humidity, 55 ± 5%; 12 h light/dark cycle).

After 1 week of acclimatization, mice were randomly allocated to experimental groups using a random number table. Investigators responsible for outcome assessment and data analysis were blinded to group allocation throughout the study. All animal procedures were conducted in accordance with the 3R principles and were approved by the Institutional Animal Care and Welfare Committee of Guangxi University of Chinese Medicine (approval no. DW20230830-166).

2.2. Main Reagents

Reagents used in this study encompassed 4% paraformaldehyde universal tissue fixative (BL539A; Labgic, Beijing, China), saturated Oil Red O staining solutions (G1015; Servicebio, Wuhan, China), and hematoxylin and eosin staining solutions (G1076; Servicebio). Triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) assay kits (A110-1-1, A111-1-1, A112-1-1, and A113-1-1) were purchased from Jiancheng Bioengineering Institute (Nanjing, China). The nitric oxide (NO) assay kit (S0024) was bought from Beyotime (Shanghai, China), and the l-Arginine (l-Arg) ELISA kit (F8018-B) was provided by Shfksc (Shanghai, China). The ARG1 antibody (RMAB60269) was supplied by Bioswamp (Wuhan, China).

2.3. Computational Procedures

2.3.1. Screening of Blood-Absorbed Components and Potential Targets of GXBD

The blood-absorbed components of GXBD were defined based on experimentally validated evidence reported in previously published pharmacokinetic and metabolomic studies, rather than predicted solely by in silico ADME models. Specifically, plasma-detected constituents of GXBD were obtained from a UPLC/Q-TOF-MS-based metabolomic analysis that systematically characterized blood-absorbed prototype compounds and metabolites of Gualou-Xiebai-Baijiu Decoction in vivo.

The DCABM-TCM module within the BATMAN-TCM platform (http://bionet.ncpsb.org.cn/dcabm-tcm) was used as a secondary literature-curation resource to assist in organizing and cross-referencing compounds that have been experimentally detected in blood, rather than as the primary source of compound identification. Accordingly, blood-absorbed components included in this study were selected based on direct experimental detection in vivo, without applying additional in silico oral bioavailability (OB), drug-likeness (DL), or scoring thresholds.

Subsequently, the canonical SMILES of each selected compound were retrieved and then submitted to the SwissTargetPrediction database (http://www.swisstargetprediction.ch/) for predicting the potential targets.

2.3.2. Prediction of AS-Related Targets

With “atherosclerosis” as the keyword, AS-related targets were searched in OMIM (https://www.omim.org/), GeneCards (https://www.genecards.org/), and open targets platform (https://platform.opentargets.org/) databases. The results were merged, followed by deduplication to obtain AS-related targets.

2.3.3. Protein–Protein Interaction Network Analysis

The targets of AS and GXBD were intersected. The intersecting targets were imported into the STRING database (https://string-db.org/), and proteins with an interaction score <0.40 were excluded as discrete proteins. The resultant PPI network was visualized with Cytoscape, and the topological analysis was performed with the CytoNCA plugin. Targets with closeness centrality (CC), betweenness centrality (BC), and degree centrality (DC) values above the median were screened as core targets.

2.3.4. Gene Ontology Functional Enrichment and Kyoto Encyclopedia of Genes and Genomes Pathway Analyses

The intersecting targets were imported into the DAVID database (https://david.ncifcrf.gov/) for GO functional enrichment and KEGG pathway enrichment analyses. Visualization was performed with the online tool at http://www.bioinformatics.com.cn.

2.3.5. Expression Quantitative Trait Loci Data for Component Targets and Genome-Wide Association Study Data for AS

Publicly available blood gene expression data were collected from the GTEx database (https://www.gtexportal.org/home/), among which eQTL data for targets of blood-absorbed components in GXBD were extracted. GWAS summary statistics for AS (GCST90475989) were acquired from the GWAS Catalog (https://www.ebi.ac.uk/gwas/), which includes 20,858 European AS cases and 405,668 European controls.

2.3.6. Selection of Instrumental Variables

To obtain a sufficient number of instrumental variables for Mendelian randomization analysis, genome-wide blood expression quantitative trait loci (eQTLs) associated with each target gene were selected using a significance threshold of P < 1 × 10–5, which is commonly adopted in exploratory and hypothesis-generating MR studies when more stringent thresholds (e.g., P < 5 × 10–8) yield an insufficient number of instruments. This strategy was applied to balance instrument strength and coverage across candidate targets.

To minimize bias due to linkage disequilibrium, SNPs were clumped using parameters of R 2 < 0.1 within a 100 kb window, retaining the SNP with the lowest P value in each locus. The strength of each instrumental variable was evaluated by calculating the F-statistic, and all selected SNPs exhibited F-statistics greater than 10, indicating sufficient instrument strength and a low risk of weak-instrument bias.

2.3.7. Two-Sample MR Analysis

For assessing the causal relationship between AS (the outcome) and the targets of blood-absorbed components (exposure), the MR analysis was performed primarily with the inverse variance weighted (IVW) method, which provides an efficient estimate of the causal relationship between exposure and outcomes by weighting SNP–outcome associations with their inverse variance. MR Egger, weighted median, simple mode, and weighted mode methods were used as complementary approaches to further evaluate the causal relationship. Cochran’s Q statistic was harnessed to analyze heterogeneity in IVW and MR Egger analyses. Specifically, results were considered homogeneous when Q_P > 0.05. Horizontal pleiotropy was determined using the MR-Egger intercept, with P > 0.05 indicating no pleiotropy. This analysis was conducted with “twosampleMR” and “Mendelianrandomization” packages in R.

2.3.8. Molecular Docking

Molecular docking was conducted using YASARA Dynamics v25.1.13 with the built-in AutoDock Vina macro (dock_run.mcr) to predict the binding affinities between the blood-absorbed components of GXBD and the key target ARG1. The crystal structure of human ARG1 (UniProt ID: P05089; PDB ID: 2AEB), which is cocrystallized with the native inhibitor 2­(S)-amino-6-boronohexanoic acid (ABH), was obtained from the RCSB PDB. All crystallographic water molecules, cocrystallized ligands, and irrelevant ions were removed, and missing hydrogen atoms and side chains were added under physiological pH conditions. Ligand structures were retrieved from the PubChem database, converted into PDB format, and optimized using the built-in Ligand Preparation tool. To avoid prior bias in binding-site selection, a fully blind docking strategy was adopted. YASARA automatically generated a docking grid covering the entire protein surface, allowing unbiased screening of all potential binding pockets. Clustering of docking poses was performed with a 5.0 Å RMSD cutoff, which is appropriate for blind docking because structurally distant minima across the full receptor surface must first be grouped before pocket-level refinement. For reproducibility, all raw docking poses were retained. After docking, the top-ranked low-energy poses were predominantly enriched in the same region as the known catalytic site of ARG1 (GLU42, GLU44, HIS141, ALA307, ASN311, and HIS312), confirming that the blind docking strategy successfully located the biologically relevant active pocket. The receptor was treated as rigid, while ligands were fully flexible. Predicted binding affinities (kcal/mol) and interacting residues were recorded for downstream analyses.

2.3.9. MD Simulations

MD simulations were performed in YASARA with the AMBER14 force field. The optimal docking conformation of the ARG1–ligand complex was solvated in a TIP3P water box with at least 10 Å of padding, and 0.9% NaCl was added to neutralize the system and mimic physiological ionic strength. Long-range electrostatics were processed using the particle mesh Ewald method, with a cutoff radius of 8 Å. The system was equilibrated by steepest descent and simulated annealing minimization and then simulated for 100 ns under periodic boundary conditions in the NPT ensemble at 310 K and pH 7.4, with a 2.5 fs integration step and trajectory output every 100 ps. YASARA built-in modules were utilized to analyze total energy, radius of gyration (Rg), RMSD, RMSF, hydrogen bond counts, and binding free energy (MM/GBSA), ensuring reproducibility with standard YASARA MD macros.

2.4. Preparation of Decoction and Animal Model

2.4.1. Decoction Preparation

According to “synopsis of prescriptions of the golden chamber” (Jin Gui Yao Lue), the decoction consists of one Gualou fruit, 0.5 sheng of Xiebai, and 7 sheng of white wine. Based on the classical prescription dosage conversion standard proposed by Academician Tong Xiaolin, the ratio of Gualou to Xiebai in the formula was approximately 1:1. In the classic prescription, “white wine” refers to modern rice wine (1 sheng is equivalent to approximately 200 mL). The crude drug pieces of TCM were acquired from Tongrentang (Beijing, China), and 10% Vol rice wine was purchased from Yixiangchun Liquor Co., Ltd. (Guizhou, China). Gualou and Xiebai at the prescribed ratio were soaked in rice wine for 30 min, then boiled over high heat, and simmered for 40 min. The mixture was then filtered and concentrated to obtain GXBD at a final concentration of 1.3 g/mL.

2.4.2. Animal Model Establishment

A total of 33 male Apoe–/– mice were housed in separate cages and acclimatized for 1 week. Eleven mice were given a standard diet (the blank group), while the remaining 22 mice were fed a high-fat diet (2% cholesterol, 10% lard, 0.5% sodium cholate, 10% egg yolk powder, 0.1% propylthiouracil, 5% sucrose, and 72.4% basic feed) for 19 weeks. ,

2.4.3. Grouping and Administration

The 22 modeled mice were randomly assigned to the model and GXBD groups. Mice in the blank and model groups were gavaged with an equal volume of sterile 0.9% sodium chloride solutions, while mice in the GXBD group were gavaged with GXBD (200 μL/mouse/day). All mice were gavaged once daily for four consecutive weeks. At the end of the experiment, mice were euthanized, and the aorta and serum were collected for later analysis.

2.5. Histological and Biochemical Assays

2.5.1. Oil Red O Staining

Aortas were fixed in 4% paraformaldehyde for over 24 h, washed twice with PBS, and stained with Oil Red O solutions for 60 min. The lesioned areas of the aortas were determined with ImageJ software.

2.5.2. Hematoxylin–Eosin Staining

Aortic tissues were fixed in 4% paraformaldehyde (room temperature) for more than 24 h, paraffin-embedded, sectioned, and stained with H & E solutions. The morphology of arterial walls was observed under an upright brightfield microscope.

2.5.3. Measurement of Blood Lipid Levels

After serum was attained, the levels of TG, TC, HDL-C, and LDL-C were measured as instructed in the protocols of commercial assay kits.

2.5.4. Determination of Serum Total NO Levels and l-Arg Expression

Following serum collection, total NO production was assessed indirectly by measuring its stable metabolites, nitrite and nitrate (NO x ), using a commercial assay kit according to the manufacturer’s instructions. Free NO was not measured directly due to its instability and short half-life. Serum l-Arg levels were determined using a corresponding ELISA kit following the manufacturer’s protocols.

2.5.5. Detection of ARG1 Expression in the Aorta

Aortic samples were lysed with radio-immunoprecipitation assay buffers, and the supernatant was attained following centrifugation. The protein concentration was determined with a bicinchoninic acid kit. Proteins were separated through sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred onto polyvinylidene fluoride membranes. After blocking, the membranes were incubated overnight (4 °C) with primary antibodies against ARG1 (1:1000) and Tubulin (1:10000). Subsequent to washing, the membranes were incubated with secondary antibodies at room temperature for 60 min. Bands were visualized with an electrogenerated chemiluminescence detection kit, and the grayscale intensity was analyzed with ImageJ software to calculate the relative expression of the target proteins.

2.6. Statistical Analysis

The statistical analysis was performed with GraphPad Prism 10.3.1 software. Data were presented as mean ± standard deviation. For data conforming to normal distribution and homogeneity of variance, two-group comparisons were performed with the t-test, and multiple-group comparisons were analyzed with one-way analysis of variance. For data with skewed distribution, nonparametric tests were used for comparisons. Differences with P < 0.05 were considered statistically significant.

3. Results

3.1. Screening of Chemical Components and Corresponding Targets of GXBD

A total of six blood-absorbed components of GXBD were identified with the BATMAN-TCM database (Table ). The potential targets of these components were predicted with the SwissTargetPrediction database, yielding 379 targets (Figure ).

1. Information on the Active Chemical Constituents of Gualou-Xiebai-Baijiu Decoction.

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Gualou-Xiebai-Baijiu Decoction-active component-target network.

3.2. Prediction and Screening of AS-Related Targets

A total of 1975 AS-related targets were identified with GeneCards, OMIM, and Open Targets Platform databases. Subsequently, 154 overlapping targets were yielded after the intersection of AS-related targets with the targets of GXBD (Figure A).

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Results of network pharmacology analysis. (A) Venn diagram showing the 154 overlapping targets between the targets of GXBD and atherosclerosis. (B) Screening of core targets of GXBD in the treatment of atherosclerosis. (C) GO functional enrichment analysis results. (D) KEGG pathway enrichment analysis results.

3.3. PPI Network Analysis of Overlapping Targets

The 154 overlapping targets were imported into the STRING database, generating a PPI network comprising 154 nodes and 1853 edges. The obtained TSV data were imported into Cytoscape software, and 64 core targets with BC, CC, and DC values above the median were identified with the CytoNCA plugin (Figure B).

3.4. Results of GO Functional Enrichment and KEGG Pathway Analyses

The GO functional enrichment analysis revealed that the targets were involved in multiple AS-associated biological processes, such as positive regulation of transcription by RNA polymerase II, negative regulation of apoptotic process, and inflammatory response (Figure C). In terms of cellular components, the targets were mainly related to plasma membrane, extracellular space, and extracellular region (Figure C). Regarding molecular function, the targets were primarily enriched in protein binding, ATP binding, protein kinase activity, and protein tyrosine kinase activity (Figure C). KEGG pathway analysis results demonstrated that the main pathways enriched included fluid shear stress and AS, PI3K-Akt pathway, and focal adhesion, all of which are closely linked to AS (Figure D).

3.5. Instrumental Variable Selection and MR Analysis Results

When the significance threshold for blood genomic data was set at P < 1 × 10–5, no SNPs were extracted for 300 targets. Among the remaining 79 targets, 34 had more than three selected SNPs. Accordingly, the causal relationship between the targets of blood-absorbed components in GXBD and AS was primarily analyzed with the IVW method based on these 34 targets.

MR analysis results displayed that six genes, namely ARG1, CCR3, EPHB4, MAPK9, MCL1, and PDK1, had marked causal associations with AS. Specifically, AS was correlated negatively with CCR3 (P = 0.037, odds ratio [OR] = 0.965), EPHB4 (P = 0.002, OR = 0.952), and PDK1 (P = 0.004, OR = 0.861) but positively with ARG1 (P = 3.00 × 10–7, OR = 1.139), MAPK9 (P = 0.035, OR = 1.068), and MCL1 (P = 0.036, OR = 1.094) (Table , Figure ). The sensitivity analysis and heterogeneity testing exhibited no evidence of pleiotropy or heterogeneity (Table ).

2. Causal Relationship between Different Drug Targets and AS Based on the MR Method.

exposure factors fate nSNP method P OR
ARG1 AS 7 MR egger 0.173 1.083
ARG1 AS 7 weighted median 5.00E-05 1.132
ARG1 AS 7 inverse variance weighted 3.00E-07 1.139
ARG1 AS 7 Simple mode 0.012 1.204
ARG1 AS 7 weighted mode 0.019 1.112
CCR3 AS 11 MR egger 0.973 1.002
CCR3 AS 11 weighted median 0.108 0.973
CCR3 AS 11 inverse variance weighted 0.037 0.965
CCR3 AS 11 simple mode 0.129 0.958
CCR3 AS 11 weighted median 0.272 0.977
EPHB4 AS 9 MR egger 0.099 0.908
EPHB4 AS 9 weighted median 0.010 0.951
EPHB4 AS 9 inverse variance weighted 0.002 0.952
EPHB4 AS 9 simple mode 0.891 0.996
EPHB4 AS 9 weighted mode 0.055 0.952
MAPK9 AS 4 MR egger 0.413 0.791
MAPK9 AS 4 weighted median 0.024 1.086
MAPK9 AS 4 inverse variance weighted 0.035 1.068
MAPK9 AS 4 simple mode 0.209 1.084
MAPK9 AS 4 weighted mode 0.127 1.085
MCL1 AS 6 MR egger 0.676 1.099
MCL1 AS 6 weighted median 0.061 1.104
MCL1 AS 6 inverse variance weighted 0.036 1.094
MCL1 AS 6 simple mode 0.165 1.113
MCL1 AS 6 weighted mode 0.160 1.109
PDK1 AS 3 MR egger 0.663 1.134
PDK1 AS 3 weighted median 0.027 0.876
PDK1 AS 3 inverse variance weighted 0.004 0.861
PDK1 AS 3 simple mode 0.216 0.873
PDK1 AS 3 weighted mode 0.196 0.886
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MR analysis results of different drug targets and AS.

3. Heterogeneity and Pleiotropic Tests between Different Drug Targets and AS.

exposure factors
 fate
 heterogeneity (Q_P)
 pleiotropism
    MR egger IVW intercept SE P
ARG1 AS 0.972 0.894 0.013 0.011 0.292
CCR3 AS 0.040 0.051 –0.012 0.020 0.575
EPHB4 AS 0.506 0.510 0.015 0.015 0.359
MAPK9 AS 0.933 0.593 0.080 0.060 0.316
MCL1 AS 0.971 0.991 –0.001 0.033 0.985
PDK1 AS 0.888 0.415 –0.044 0.034 0.413
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3.6. Key Target of GXBD Involved in AS Treatment

The six targets with a significant causal relationship to AS identified by the MR analysis were further intersected with the 64 core targets screened by the network pharmacology analysis, which yielded one overlapping target, ARG1 (Figure A). This result suggests that ARG1 may be the most critical target of GXBD involved in AS treatment. Furthermore, heterogeneity testing and sensitivity analyses demonstrated that the causal relationship between ARG1 and AS remained statistically robust (Figure B–E).

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Correlation analysis between network pharmacology and MR results. (A) Venn diagram of the intersection between the six targets with a significant causal relationship to AS identified by the MR analysis and the 64 core targets screened by the network pharmacology analysis. (B) Scatter plot of causal effects between ARG1 and AS in the MR analysis. (C) Forest plot of the causal effects of each SNP in ARG1 on AS. (D) Sensitivity analyses of the relationship between ARG1 and AS. (E) Funnel plot of SNP-bias for the relationship between ARG1 and AS.

3.7. Molecular Docking and MD Simulations

The blood-absorbed components of GXBD were subjected to molecular docking with ARG1, and all initial binding energies were below −6 kcal/mol (Table ), illustrating that these compounds harbored strong binding potential to ARG1. Specifically, Chrysoeriol glucuronide formed hydrogen bonds, π–anion interactions, and hydrophobic contacts with residues such as GLU42, GLU44, ASN311, and ALA307, which were fully embedded into the binding pocket and surrounded by side-chain residues (Figure A). Furosta-5,20(22)-diene-3,26-diol achieved stable binding to ARG1 by forming a hydrogen bond with GLU262 and engaging in hydrophobic/π–alkyl interactions with TYR259, LYS266, ALA307, and HIS312 (Figure B). (R)-2,3,4,9-tetrahydro-1H-pyrido­[3,4-b]­indole-3-carboxylic acid formed hydrogen bonds, electrostatic interactions, and hydrophobic interactions with residues ASP158, PHE162, SER163, and ILE156, constructing a semienclosed binding cavity (Figure C). Sceptrumgenin bound to ARG1 mainly by relying on hydrophobic contacts with residues VAL30, VAL289, and VAL293, but the binding cavity was relatively open, hinting at limited stability (Figure D). Psi-diosgenin achieved transient stabilization through hydrogen bonding with ASN311 and HIS312, as well as multiple hydrophobic side-chain contacts; nevertheless, the binding mode showed a drifting tendency over extended time scales (Figure E). Pseudohecogenin formed hydrogen bonds with GLU186 and THR246 and hydrophobic interactions with HIS126, HIS141, and LYS68, generating a “hydrophobic core plus hydrogen-bond constraint” binding mode with strong stability (Figure F). Overall, hydrogen bonding, hydrophobic interactions, and electrostatic effects led to diverse binding modes of different components within the ARG1 pocket, providing the structural basis for subsequent validation by MD simulations.

4. Basic Information of Key Compounds in Gualou-Xiebai-Baijiu Decoction and their Free Binding Energy with Key Targets of AS.

compound chemical formula molecular weight (g/mol) target free binding energy (kcal/mol)
Chrysoeriol glucuronidates C22H20O12 476.4 ARG1 –7.63
Furosta-5,20(22)-diene-3,26-diol C27H42O3 414.6 ARG1 –7.41
(R)-2,3,4,9-tetrahydro-1h-pyrido[3,4-b]indole-3-carboxylic acid C12H12N2O2 216.24 ARG1 –6.25
Sceptrumgenin C27H40O3 412.6 ARG1 –7.66
Psi-diosgenin C27H42O3 414.6 ARG1 –7.32
Pseudohecogenin C27H42O4 430.6 ARG1 –6.83
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Molecular docking and MD simulations of six blood-absorbed components in GXBD with ARG1. (A–F) Docking conformations of Chrysoeriol glucuronide, Furosta-5,20(22)-diene-3,26-diol, (R)-2,3,4,9-tetrahydro-1H-pyrido­[3,4-b]­indole-3-carboxylic acid, Sceptrumgenin, Psi-diosgenin, and Pseudohecogenin with ARG1. (G–L) MD simulation trajectories of the six ARG1–compound complexes over 100 ns, showing RMSD and binding energy profiles.

The binding stability of ARG1–compound complex under dynamic conditions was assessed through 100 ns MD simulations. The results showed that the total energies of all systems were maintained within a stable negative range, and protein conformations remained compact, with favorable hydration states, an RMSF value of <3 Å, and only the C-terminal region exhibiting moderate flexibility, underscoring good thermodynamic and structural stability of the complexes (Figure G–L). Specifically, the complexes of C. glucuronide and Furosta-5,20(22)-diene-3,26-diol exhibited stable energies, with RMSD converging within 2–4 Å and binding free energies maintained at approximately −118 kcal/mol and −66 kcal/mol, respectively, reflecting the strong stability (Figure G,H). The (R)-2,3,4,9-tetrahydro-1H-pyrido­[3,4-b]­indole-3-carboxylic acid complex reached a stable state after 30 ns, with an average binding free energy of around −100 kcal/mol (Figure I). Although Sceptrumgenin exhibited stable energies and conformations, its binding energy was positive (∼36 kcal/mol), highlighting the weak binding affinity (Figure J). The binding free energies of Psi-diosgenin were initially maintained within the negative range (−30 to −200 kcal/mol) during the first 50 ns but shifted to positive values later, indicating a dissociation tendency (Figure K). The Pseudohecogenin complex presented stable energies and structures, with an average binding free energy of approximately −186 kcal/mol, suggesting the strongest binding affinity (Figure L). Taken together, C. glucuronide and Pseudohecogenin displayed the most favorable binding interactions with ARG1, and Furosta-5,20(22)-diene-3,26-diol and (R)-2,3,4,9-tetrahydro-1H-pyrido­[3,4-b]­indole-3-carboxylic acid also showed good binding stability to ARG1. Nonetheless, Sceptrumgenin and Psi-diosgenin had relatively weaker binding capacities to ARG1.

3.8. In Vivo Validation of MR Analysis Results

3.8.1. Oil Red O Staining of Mouse Aortas

As displayed in Oil Red O staining results, the aortic plaque area was relatively small in the blank group and was markedly larger in the model group than in the blank group. Compared with the model group, the GXBD group showed an obvious reduction in the aortic plaque area (Figure A,B).

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Validation of MR analysis results in mice. (A,B) Oil Red O staining of the full-length mouse aorta (n = 5, mean ± standard deviation). (C–F) Serum levels of triglycerides, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol in mice (n = 5, mean ± standard deviation). (G) Serum levels of total NO in mice (n = 5, mean ± standard deviation). (H) Relative levels of l-Arg in mouse serum (n = 5, mean ± standard deviation). (I,J) Relative expression of ARG1 protein in the mouse aorta (n = 3, mean ± standard deviation). (K) Hematoxylin and eosin staining of the mouse aorta.

Data are presented as mean ± standard deviation (SD). Statistical analysis was performed using [Student’s t-test/one-way ANOVA, specify as appropriate]. Statistical significance is indicated as *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

3.8.2. H&E Staining of Mouse Aortas

H & E staining revealed that in the blank group, the elastic fibers of the aortic media were arranged in a regular wavy pattern, with clear stratification, no fiber rupture, a small number of spindle-shaped fibroblasts in the interstitial spaces, and even staining of nuclei. In the model group, the aortic media became markedly loose, accompanied by irregular wavy arrangement of elastic fibers and the formation of cystic spaces in some regions, indicating fiber rupture and matrix liquefaction, which are typical features of medial cystic necrosis. In the GXBD group, partial rupture and local separation of the elastic fiber layers were observed in the aortic media, suggesting mild pathological changes in the mouse aorta (Figure K).

3.8.3. Serum Lipid Levels in Mice

Compared with the blank and GXBD groups, the model group showed substantially increased serum levels of TG, TC, and LDL-C and prominently reduced serum levels of HDL-C (Figure C–F).

3.8.4. Levels of the ARG1–l-Arg–NO Pathway in Mice

As presented in Western blot result, ARG1 protein levels in the aorta were remarkably higher in the model group than in the blank and GXBD groups (Figure I,J). According to ELISA results, serum l-Arg levels were obviously lower in the model group than in the blank and GXBD groups (Figure H). Additionally, serum NO levels in the model group were significantly lower than those in the blank and GXBD groups (Figure G).

4. Discussion

Although previous studies reported that GXBD modulated lipid metabolism, inflammatory responses, and vascular function in the context of AS, − there is limited direct causal evidence linking the components of GXBD to specific molecular targets in AS. In this study, we harnessed an integrative approach combining network pharmacology, MR analyses, molecular docking, and MD simulations to explore the potential mechanisms of action of GXBD in AS treatment. Our computational results were further corroborated by preliminary in vivo validation in ApoE–/–mice.

Network pharmacology results displayed that GXBD might act through multiple targets, including ARG1, CCR3, EPHB4, MAPK9, MCL1, and PDK1, which were involved in AS-related pathways such as fluid shear stress, PI3K-Akt, and focal adhesion signaling. These pathways have been reported to be closely related to endothelial cell homeostasis, vascular tone, and inflammatory activation. − Among them, ARG1 was determined as the most critical target, consistent with MR analysis results showing a positive causal relationship between ARG1 and AS. ARG1 competes with NOS for l-Arg and thereby reduces NO production, ultimately impairing endothelial function. In the vascular context of atherosclerosis, NOS is considered the primary NOS isoform responsible for vascular NO production and endothelial homeostasis, and the NOS referred to in this study mainly reflects eNOS activity. − Accordingly, ARG1 inhibition represents a plausible mechanism for improving vascular function in AS.

Consistent with this prediction, our animal experiments demonstrated that GXBD administration lowered ARG1 protein expression in the aorta, increased serum l-Arg and NO levels, and reduced vascular plaque formation in ApoE–/–mice. GXBD also improved lipid profiles by lowering TG, TC, and LDL-C levels and elevating HDL-C levels. These findings provide preliminary evidence that GXBD can attenuate AS-related pathological changes. However, it should be noted that the GXBD intervention was conducted for four consecutive weeks, which was designed to evaluate early therapeutic modulation of atherosclerotic pathology rather than definitive plaque regression. Longer-term treatment studies will be required to determine whether GXBD can induce sustained plaque stabilization or regression. Therefore, these results should be interpreted with caution and should not be equated with definitive therapeutic efficacy. Although the present findings support an association between GXBD treatment and modulation of the ARG1/l-Arg/NO pathway, the mechanistic interpretation is primarily based on correlative evidence. Direct causal validation, such as pharmacological inhibition or genetic knockdown of ARG1, as well as quantification of additional pathway components including eNOS and ARG2, will be required to conclusively establish pathway involvement. Furthermore, assessment of inflammatory and oxidative stress markers (e.g., TNF-α and IL-6) would provide complementary evidence for downstream functional effects.

In addition, our analyses identified C. glucuronide as one compound in GXBD that targeted ARG1. Molecular docking and MD simulations revealed a stable interaction between C. glucuronide and ARG1, raising the possibility that C. glucuronide contributes to the activity of GXBD. Nevertheless, GXBD is a classical TCM formula that is applied clinically as a whole prescription, and its effects are generally attributed to the synergistic actions of multiple constituents, rather than a single molecule. For this reason, the whole decoction was tested in vivo to better reflect traditional and clinical practices. Importantly, we did not directly detect C. glucuronide in plasma or tissues, and its safety profile and pharmacokinetics remain uncharacterized. Hence, the proposed role of this compound should be considered hypothetical, and future studies are warranted to isolate and test it directly, compare it with established antiatherosclerotic drugs, and evaluate its toxicity. Furthermore, we expanded the modeling analysis to include all six active components in GXBD. Comparative molecular docking and MD simulations revealed that, in addition to C. glucuronide, Furosta-5,20(22)-diene-3,26-diol, (R)-2,3,4,9-tetrahydro-1H-pyrido­[3,4-b]­indole-3-carboxylic acid, and Pseudohecogenin also presented favorable binding stability and energy parameters with ARG1. This finding supports the notion that the effects of GXBD may derive from the combined action of multiple constituents, consistent with the multicomponent and synergistic nature of TCM formulas.

Of note, several limitations should be acknowledged. First, although GXBD was prepared strictly according to the classical prescription and standardized procedures, the chemical composition and concentration consistency of the decoction were not quantitatively characterized by HPLC or LC–MS. This limits rigorous quality control and batch-to-batch comparison, and future studies incorporating systematic chemical profiling will be necessary to strengthen translational robustness. In addition, network pharmacology relies on the completeness and accuracy of existing databases; molecular docking provides static predictions of ligand–target interactions; MR analysis may be influenced by pleiotropy and the availability of valid instrumental variables; and MD simulations are constrained by force field parameters and simulation time scales. Combined with the short duration of animal experiments, these limitations highlight the need for more comprehensive validation, including long-term in vivo studies and direct quantification of candidate compounds in tissues.

Second, another limitation of the present study is the relatively small sample size used for biological validation, particularly for Western blot analysis (n = 3) and serum assays (n = 5). While these sample sizes are commonly adopted in exploratory in vivo studies, they may not fully capture biological variability. Therefore, the current findings should be interpreted as preliminary and hypothesis-generating, and future studies with larger cohorts will be required to confirm the robustness and reproducibility of these observations.

Third, although quantitative analysis of aortic lesion area based on Oil Red O staining has been incorporated, the histological assessment remains limited to two-dimensional surface measurements and representative sections. More comprehensive plaque characterization, including lesion composition, collagen content, and inflammatory cell infiltration, will be required in future studies to better define plaque stability and regression. In addition, the present study focused on ARG1, as it was the only target consistently supported by both network pharmacology and Mendelian randomization analyses. However, other key components of the l-Arg/NO pathway, including endothelial nitric oxide synthase (NOS3/eNOS) and ARG2, were not examined. The absence of these markers limits a more complete mechanistic interpretation of pathway-level regulation and should be addressed in future investigations. Furthermore, l-arginine and nitric oxide production were assessed only in serum, whereas endothelial NO synthesis occurs locally within vascular tissues. Circulating levels of l-arginine and NO metabolites may not fully reflect endothelial eNOS activity or localized NO bioavailability. Therefore, serum measurements alone provide an incomplete assessment of endothelial function, and future studies incorporating tissue-specific analyses will be essential.

Fourth, only male ApoE–/– mice were used in this study. Although this model is widely applied in atherosclerosis research, sex-specific differences in lipid metabolism, vascular inflammation, and plaque progression have been well documented. Therefore, the exclusive use of male mice represents a limitation, and future studies incorporating female mice will be necessary to determine whether the antiatherosclerotic effects of GXBD are sex dependent.

Fifth, rice wine was used as a processing and extraction medium according to the classical formulation of GXBD. Although the ethanol content in the final decoction was relatively low and consistent across treatments, a rice wine–only vehicle control was not included. As such, a potential contribution of ethanol to the observed effects cannot be completely excluded and should be addressed in future studies by incorporating an appropriate vehicle control. However, previous evidence indicates that ethanol per se does not significantly ameliorate established atherosclerosis, as neither ethanol nor red wine polyphenols reduced advanced lesions in ApoE–/– mice, implying that the solvent contribution of ethanol is unlikely to be the primary driver of therapeutic effects.

Sixth, our study did not include a head-to-head comparison between C. glucuronide and established antiatherosclerotic drugs. Such benchmarking would provide more rigorous evidence of therapeutic potential and should be addressed in future work. The safety profile of C. glucuronide remains uncharacterized, as specific toxicological datasuch as LD50 values or hepatotoxicity assessmentsare currently lacking. In addition, systematic toxicity assessments of GXBD itself were not performed in the present study, such as histological examination of liver and kidney tissues or measurement of serum biochemical markers. Therefore, potential hepatic or renal toxicity cannot be fully excluded and should be evaluated in future studies. Nevertheless, it is worth noting that this compound is derived from Gualou Xiebai Baijiu Decoction (GXBD), a classical traditional Chinese medicine (TCM) formula that has been used in clinical practice for decades to treat cardiovascular diseases with an overall favorable safety record. The primary objective of this study was to elucidate the bioactive constituents responsible for GXBD’s therapeutic effects, rather than to propose immediate clinical application of a single isolated molecule. However, we fully acknowledge that systematic toxicity and safety evaluations will be essential for future development of C. glucuronide and other GXBD-derived compounds (e.g., Furosta-5,20(22)-diene-3,26-diol, (R)-2,3,4,9-tetrahydro-1H-pyrido­[3,4-b]­indole-3-carboxylic acid, and Pseudohecogenin) as potential therapeutic agents for atherosclerosis.

5. Conclusion

In summary, this study comprehensively analyzed the effective blood-absorbed components, key targets, and potential molecular mechanisms of action of GXBD in AS treatment by using network pharmacology, MR analyses, molecular docking, MD simulations, and animal experiments. The results unravel that GXBD may exert a protective effect against AS by reducing ARG1 expression and enhancing NO release, with C. glucuronide predicted to play a contributory role in this effect. These findings provide new scientific evidence for the anti-AS effect of GXBD and offer a novel research strategy for the application of complementary medicine in AS treatment.

The animal experiment was conducted according to the standard ethical guidelines that were approved by the Ethics Committee of Guangxi University of Chinese Medicine Institutional Welfare and Ethical Committee (grant no. DW20230830-166).

§.

Y.C. and K.H. have contributed equally to this work. Y.C.: performed the research, provided and analyzed the data, and drafted the manuscript. K.H.: designed and performed the research, and maintained the study animals. Y.H.: designed and performed the research. R.L.: performed experiments and carried out formal analysis. Y.M.: performed experiments and edited the manuscript. S.N.: performed experiments. Y.T.: conceptualization, designed the methodology, organized and supervised the research, and edited the paper.

This work was supported by the National Natural Science Foundation of China [grant no. 82160856, 2022]; and Guangxi Key Technologies R & D Program [grant no. 2022AB11054, 2023]; and Guangxi Natural Science Foundation [grant no. 2023JJD140055, 2024].

The authors declare no competing financial interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The animal experiment was conducted according to the standard ethical guidelines that were approved by the Ethics Committee of Guangxi University of Chinese Medicine Institutional Welfare and Ethical Committee (grant no. DW20230830-166).

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