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Method Article

Exploring the Mechanism of San Jie Tong Mai Formula Against Atherosclerosis via Network Pharmacology and Proteomics

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DOI:

10.3791/70080

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April 17th, 2026

 ,  ,  ,  ,  , 
1Department of Cardiology, the Third Affiliated Clinical Hospital of Changchun University of Chinese Medicine, 2Department of Respiratory, The Affiliated Hospital of Changchun University of Chinese Medicine, 3The Affiliated Hospital of Changchun University of Chinese Medicine

In This Article

Summary

This study demonstrates that San Jie Tong Mai Formula attenuates atherosclerosis in ApoE-/- mice by regulating Hsd11b1-mediated glucocorticoid metabolism, thereby improving dyslipidemia and vascular inflammation. Integrated network pharmacology, proteomics, and molecular docking analyses reveal a multi-component, multi-target synergistic mechanism underlying its anti-atherosclerotic effects.

Abstract

This study aimed to elucidate the mechanism of San Jie Tong Mai Formula (SJTMF) against atherosclerosis (AS), a leading cause of cardiovascular morbidity. Using ApoE-/- mouse models, we demonstrated that SJTMF significantly inhibits AS plaque progression. Through an integrated network pharmacology and proteomics strategy, five core bioactive components were identified: beta-sitosterol, naringenin, luteolin, isorhamnetin, and 3beta-hydroxy-24-methylene-8-lanostene-21-oic acid. Concurrently, proteomics revealed 129 AS-related proteins that were differentially expressed. Molecular docking confirmed high-affinity binding interactions between these components and the key target Hsd11b1, with their binding energies all below -5 kcal/mol. Mechanistic investigations further revealed that SJTMF may regulate Hsd11b1-mediated glucocorticoid metabolism. This regulation contributes to significant amelioration of both dyslipidemia and vascular inflammation, thereby suppressing AS development. Collectively, this work demonstrates, for the first time, the innovative mechanism by which a traditional Chinese medicine formula exerts anti-AS effects through multi-component synergistic regulation of the Hsd11b1 target, offering new insights for therapeutic intervention.

Introduction

Atherosclerosis (AS), a chronic inflammatory disease affecting systemic vasculature, constitutes the primary pathological basis for cardiovascular and cerebrovascular events1. Its pathogenesis involves complex interactions among genetic, environmental, psychosocial, and multifactorial pathological factors, exhibiting multistage and polygenic characteristics. Although current therapeutic strategies-particularly lipid-lowering agents such as statins and Proprotein Convertase Subtilisin/Kexin Type 9(PCSK9) inhibitors-effectively reduce low-density lipoprotein cholesterol (LDL-C) levels, they do not fully address the residual inflammatory risk that drives recurrent cardiovascular events2. Furthermore, therapy-associated adverse effects (e.g., new-onset diabetes, myotoxicity, hepatorenal impairment) limit the long-term use and intensification of lipid-lowering therapy3,4. These limitations highlight the need for complementary approaches that directly target vascular inflammation, including anti-inflammatory therapies, to further reduce cardiovascular risk beyond lipid-lowering alone.

In traditional Chinese medicine (TCM), AS falls under the categories of "chest impediment" (Xiong Bi) and "vessel accumulation" (Mai Ji), with "phlegm-stasis entanglement" (Tanyu Hujie) recognized as its core pathogenesis. Guided by the "stasis-transforming hydrotherapy" (Yuneng Huashui) principle, our research group developed the "San Jie Tong Mai Formula (SJTMF)" composed of 14 herbs, including Salvia miltiorrhiza (Danshen) and Whitmania pigra Whitman (Shuizhi), to synergistically resolve phlegm-turbidity, dissipate stasis, and promote diuresis. Preliminary clinical studies demonstrated SJTMF's efficacy in significantly improving lipid profiles and stabilizing/reversing atherosclerotic plaques in elderly patients with carotid AS5. Experimental investigations further revealed its capacity to inhibit AS progression by modulating macrophage polarization and autophagy pathways6,7. Nevertheless, the multi-component, multi-target, and multi-pathway characteristics of TCM formulas present challenges in systematically elucidating their integrated regulatory mechanisms using conventional reductionist approaches.

To explore the molecular mechanisms mediating SJTMF's anti-atherosclerotic effects and address the challenges, this study employed an integrated multidisciplinary strategy. Network pharmacology, conceptualized by Hopkins8,9, was utilized to construct a multidimensional "herbal component-target-disease pathway" network. This approach facilitated the prediction of SJTMF's primary active compounds and potential therapeutic targets for AS intervention, consistent with TCM's holistic regulatory features. Complementing this, proteomics technology, leveraging high-throughput and high-precision qualitative and quantitative protein analysis10, dynamically monitored changes in the AS-associated protein expression profile after SJTMF intervention in animal models. This enabled the identification of key effector molecules and signaling events, thereby furnishing experimental evidence for mechanistic exploration. Subsequently, high-precision molecular docking simulations were performed to evaluate the binding modes and binding energies between the core targets predicted by network pharmacology and the key bioactive components, providing a theoretical basis for potential component-target interactions11. Compared to alternative approaches, such as relying solely on network pharmacology predictions (prone to high false-positive rates and lacking experimental validation)12 or combining only proteomics with molecular docking (which may fail to capture system-level interactions predicted by network analysis), this integrated strategy synergistically addresses these inherent limitations13.

By integrating multidimensional techniques, this study aims to systematically elucidate the key pharmacodynamic material basis, core target network, and multi-pathway synergistic mechanisms of SJTMF. Thus, providing mechanism-driven theoretical support for advancing the modernization of TCM and the development of innovative drugs.

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Protocol

All animal procedures were conducted with approval from the Experimental Animals Committee of Changchun University of Chinese Medicine (Ethics Approval No. 2023521). Specific pathogen-free (SPF) male C57BL/6J and ApoE⁻/⁻ mice (5 weeks old, 18-22 g) were included in the study (Laboratory Animal Use License Nos. SCXK (JI) 2023-002 and SCXK (SU) 2021-0013). Information regarding reagents and instrumentation is provided in the Table of Materials.

1. Network pharmacology

  1. Prediction of Active Components and Targets of SJTMF
    1. Screen the active components of Salvia miltiorrhiza, Panax notoginseng, Artemisia scoparia, Poria cocos, Alisma plantago-aquatica L, Amomum villosum, Santalum albumL, Citrus aurantium, and Citrus reticulata peel using oral bioavailability (OB) ≥ 30 % and drug-likeness (DL) ≥ 0.18 as criteria.
    2. Screen the active components of Whitmania pigra Whitman, Bombyx batryticatus, Periostracum cicadae, Caulis Bambusae in Taeniam, and Ophicalcitum medicinal materials by applying Lipinski's Rule of Five, with the following parameters: miLogP ≤ 5, nOHNH ≤ 5, and nOH ≤ 10.
    3. Combine the screened compounds with their SMILES codes to predict compound targets.
      NOTE: For details on the two databases mentioned above, see the Table of Materials.
  2. Prediction of AS-related targets and construction of a "drug-component-target" network.
    1. Use the GeneCards database to retrieve AS-related disease targets using standardized disease terminology.
    2. Use Venny 2.1 software to identify the intersecting targets between SJTMF and AS as potential therapeutic targets of SJTMF for AS.
    3. Use Cytoscape 3.9.1 to construct a "drug-component-target" network diagram.
      NOTE: For details about the databases mentioned above, see Table of Materials.

2. Acclimatization

  1. House all mice in SPF facilities (24 °C; 50-60% humidity; 12 h light/dark cycle). Provide ad libitum standard chow and water. Allow 7 days for acclimatization before experiments.

3. Preparation of SJTMF

  1. Prepare the 14-herb formulation containing: Salvia miltiorrhiza (Dan Shen) 30 g, Artemisia scoparia (Yin Chen) 10 g, Panax notoginseng (San Qi) 10 g, Poria cocos (Fu Ling) 10 g, Amomum villosum (Sha Ren) 10 g, Alisma plantago-aquatica L (Ze Xie) 30 g, Santalum albumL (Tan Xiang) 10 g, Citrus reticulata peel (Chen Pi) 10 g, Citrus aurantium (Zhi Qiao) 10 g, Whitmania pigra Whitman (Shui Zhi) 10 g, Periostracum cicadae (Chan Tui) 10 g, Bombyx batryticatus (Jiang Can) 10 g, Caulis Bambusae in Taeniam (Zhu Ru) 10 g, and Ophicalcitum (Hua Rui Shi) 30 g.
  2. Decoct herbal materials (1,800 g total) 3× with 8 volumes of distilled water (1.5 h per extraction).
  3. Filter through gauze and 100-mesh nylon.
  4. Concentrate filtrate at 60 °C (density: 1.23-1.27). Dry concentrate under reduced pressure at 70 °C.
  5. Pulverize dried extract through 80-mesh sieve (yield: 20.6 %).
    NOTE: Following a previously published report7, the mice received 3.56 g∙kg⁻¹∙day⁻¹ of SJTMF extract (normalized by body surface area).

4. Model establishment

  1. Induce AS in ApoE-/- mice using a 12-week high-fat diet (HFD; 20% fat, 1.25 % cholesterol). Measure body weight at 7-day intervals for metabolic monitoring.
  2. Assign C57BL/6J mice to the Control group (Control, normal diet + saline). Randomly assign successfully modeled mice into four groups: Model group, Atorvastatin group (0.0026 mg/g atorvastatin), SJTMF-L group (low-dose SJTMF, 3.56 g∙kg-1∙day-1), SJTMF-H group (high-dose SJTMF, 7.12 g∙kg-1∙day-1)7. Administer all interventions via standardized 0.4 mL gavage in the morning for 4 weeks.
    NOTE: Stock solutions of SJTMF were prepared according to our established protocol7. Experimental interventions commenced at week 9, with morning dosing maintaining clinical temporal rhythm. Mice were defined as successfully modeled when obvious atherosclerotic plaques were observed in the aorta by gross Oil Red O staining7.

5. Sample collection, Aorta harvesting and preservation

NOTE: After 4 weeks of intragastric administration, fast all mice for 12 hours (water ad libitum). Measure final body weights, then sacrifice them via cervical dislocation under anesthesia.

  1. Incise the mouse abdomen up to the xiphoid process.
  2. Isolate the aorta from the aortic root to the common iliac artery bifurcation.
  3. Remove the excess adipose tissue and transect the aorta approximately 1 cm distal to the aortic arch.
  4. Fix the aorta in paraformaldehyde or flash-freeze it in liquid nitrogen.
    NOTE: Collect the serum and store the aortic tissues at -80 °C for future analysis.
  5. HE staining
    1. Place sections in xylene in sequence for 20 min each, twice total; then transfer to absolute ethanol for 10 min each, twice total. Hydrate sections by passing through a descending graded ethanol series (95%, 90%, 80%, 70%), 5 min per concentration.
    2. Stain in sequence with 0.4% hematoxylin for 3-8 min, then with 0.5% eosin solution for 1-3 min.
      NOTE: Rinse well with distilled water between steps.
    3. Dehydrate sections via an ascending ethanol series (95% to absolute ethanol), 5 min per concentration. Clear sections in xylene for 5 min each, twice total. Allow to air-dry fully, then mount with neutral resin.
    4. Observe under a microscope and capture images.

6. Total protein preparation from aortic tissue samples

NOTE: Proteins were isolated from the aortic tissues of each of the three experimental groups (Control group, Model group, SJTMF-H group).

  1. Homogenize aortic tissues in lysis buffer (1.5% SDS/100 mM Tris-HCl, pH 8.5), then incubate at 95 °C for 15 min to achieve protein denaturation.
  2. Cool the samples, then centrifuge them at 12,000 × g and 4 °C for 5 min and collect the supernatant. Precipitate the proteins in the supernatant using the acetone precipitation method.
  3. Redissolve the protein precipitate (8 M Urea/100 mM Tris-HCl, pH 8.5), then prepare the BCA assay kit according to the manufacturer's instructions and determine the total protein concentration  see Table of Materials).
    NOTE: With the same amount of protein among different samples, the redissolved solution was used to fill all samples to the same volume.
  4. Conduct protein reduction and alkylation with TCEP and CAA at 37 °C for 1 h.
  5. Add trypsin (1:50) to the protein sample and incubate at 37 °C for 24 hours (see Table of Materials).
  6. Use TFA to adjust the pH to 6.0 to terminate digestion. Centrifuge the sample at 12,000 × g for 15 min, then purify the peptides from the supernatant using a self-made SDB-RPS desalting column (see Table of Materials).
    NOTE: The peptide eluate was vacuum dried and stored at -20 °C for later use.

7. Liquid chromatography - tandem mass spectrometry (LC - MS/MS)

  1. Inject 200 ng of peptide digest with an injection volume of 1 µL onto a C18 trap column (75 µm × 2 cm, 3 µm, 100 Å). Peptides were then separated on a reversed-phase C18 analytical column (75 µm × 15 cm, 1.7 µm, 100 Å) at a flow rate of 0.3 µL/min.
  2. Use water containing 0.1% formic acid (FA) as mobile phase A and acetonitrile (ACN) containing 0.1% FA as mobile phase B (see Table of Materials). Apply a linear gradient from 12% to 30% B over 0-12 min, followed by an increase from 30% to 80% B over 12-15 min, and maintain at 80% B from 15-18 min for column washing. Set the flow rate to 0.300 µL/min from 0-15 min, then increase it to 0.400 µL/min from 16-18 min. Set the capillary voltage to 1500 V.
  3. Acquire MS and MS/MS spectra over the range of 100 to 1700 m/z.
  4. Scan the ion mobility over the range of 0.6 to 1.6 Vs/cm². Set the accumulation time and ramp time to 50 ms.

8. Protein function analysis

NOTE: Differentially Expressed Proteins (DEPs) from proteomics were intersected with key targets from network pharmacology, and the overlapping target proteins were selected for Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.Functional categories and pathways with P < 0.05 were considered significant.

  1. Analyze the DIA raw data in library-free mode.
  2. Search the spectra files against the Mouse protein sequence database.
  3. Set the main search parameters.
    1. Enable precursor ion generation options for in silico-predicted spectral library generation.
    2. Specify the enzyme with a maximum of 2 missed cleavages allowed (see Table of Materials).
    3. Set Carbamidomethylation of cysteine (C) as a fixed modification.
    4. Set Oxidation of methionine (M) and protein N-terminal Acetylation as variable modifications.
    5. Set the precursor mass accuracy and MS1 mass accuracy to 15 ppm.
    6. Enable Match Between Runs and Heuristic protein inference.
    7. Set the Precursor False Discovery Rate (FDR) threshold to 1% for reliable identification.
  4. Run the DIA-NN (1.8.2 beta 11) analysis.
    NOTE: Perform protein-level quantification using DIA-NN, and conduct bioinformatic analysis and visualization using R. Significantly DEPs were determined by the P value from Student's t-test and fold change thresholds.

9. Molecular docking

  1. Download the three-dimensional crystal structure of the target protein from the RCSB PDB database and save it in PDB format;meanwhile, download the molecular structure of the core active component of SJTMF from the PubChem database and save it in SDF format.
  2. Use PyMOL 2.6.0 to perform water removal and hydrogenation on the target protein structure, remove irrelevant endogenous ligands, and save the processed protein structure in PDB format; obtain the docking pocket parameters via the Getbox Plugin of PyMOL, with the grid box parameters set as follows: center_x = -7.2, center_y = 53.4, center_z = 30.6, size_x = 73.7, size_y = 85.9, size_z = 67.2, and the number of docking conformations num_modes = 10.
  3. Import the processed target protein file and the file of SJTMF's core compound file separately into AutoDock Tools 1.5.6 to convert both files into PDBQT format, then perform molecular docking with AutoDock Vina 1.1.2 based on the above-defined grid box parameters.
  4. Visualize the molecular docking results with PyMOL.

10. Statistics

  1. Perform statistical analysis.
  2. Express results as mean ± standard deviation (SD).
  3. Specify the use of Tukey's Honest Significant Difference (HSD) test for post hoc analysis after one-way ANOVA when data met normality and homogeneity of variances; apply the Games-Howell test for heteroscedastic data. Consider a p < 0.05 statistically significant.
  4. Visualize the data using GraphPad Prism8.0.2.

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Results

SJTMF suppresses atherosclerotic plaque formation in ApoE-/- mice
To investigate the inhibitory effect of SJTMF on AS, ApoE-/- mouse model was used to monitor body weight changes and observe histopathological alterations in the aorta. Compared with the Model group, SJTMF intervention ameliorated body weight gain in HFD-fed ApoE-/- mice (Figure 1A). Specifically, different doses of SJTMF showed a trend tow...

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Discussion

Although previous studies have demonstrated that SJTMF exerts anti-inflammatory effects and induces macrophage polarization in ApoE⁻/⁻ mouse models6,7, the specific molecular mechanisms underlying its anti-atherosclerotic activity remain unclear. The "same treatment for different diseases" principle in traditional Chinese medicine stems from the synergistic effects of multi-component, multi-target, and multi-pathway regulation. Netw...

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Disclosures

The authors have no conflicts of interest to declare.

Acknowledgements

This study was supported by the Natural Science Foundation of Jilin Province (Nos. YDZJ202401644ZYTS).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
2-Chloroacetamide (CAA)Sigma-AldrichC0627-100G
4% Paraformaldehyde Fix SolutionSinopharm Chemical Reagent Co., Ltd.80096618
0.5–10 µL Micropipetteeppendorf4924000029
10 – 100 µL Micropipetteeppendorf4924000053
20 – 200 µL Micropipetteeppendorf4924000061
100 – 1,000 µL Micropipetteeppendorf4924000088
1000 µL Pipette TipsNEST Life Science Co., Ltd.303216
10 µL Pipette TipsNEST Life Science Co., Ltd.301016
20 µL Pipette TipsNEST Life Science Co., Ltd.302106
15 mL Centrifuge TubeNEST Life Science Co., Ltd.601052
1.5 mL Centrifuge TubeNEST Life Science Co., Ltd.615601
A high - throughput experiment - and reference - guided database of traditional Chinese medicine(HERB)/http://herb.ac.cn/v2
Acetonitrile (LC-MS)Thermo Fisher ScientificA955-4
Acetonyl acetoneSinopharm Chemical Reagent Co., Ltd.10000418
Ammonia WaterSinopharm Chemical Reagent Co., Ltd.10011028
AutoclaveShanghai Sanshen Medical Devices Co., Ltd.YM75
Acclaim PepMap (75µm*2cm, C18, 3µm, 100Å)Thermo164535
AURORA ULTIMATE (75µm*25cm, 1.6µm, 100Å)AURORAAUR3-25075C18-CSI
BCA Protein Assay KitSangon Biotech (Shanghai) Co., Ltd.C503021-0500
Benchtop High - Speed Refrigerated CentrifugeHunan Kecheng Instrument Equipment Co., Ltd.H1 - 16KR
Bromophenol BlueSinopharm Chemical Reagent Co., Ltd.71008060
Block HeaterHangzhou Miu InstrumentsDKT-100
ChloroformSinopharm Chemical Reagent Co., Ltd.10006818
Chemiluminescence Imaging SystemHangzhou Shenhua Technology Co., Ltd.SH - 523
Clean BenchSuzhou Jinghua Equipment Co., Ltd.SW-CJ-2D
Cover GlassJiangsu Shitai Experimental Equipment Co., Ltd.10212450C
Cytoscape v3.9.1//
DIA-NN (V1.8.1)The Francis Crick Institutegithub.com/vdemichev/DiaNN
Digital Display Constant Temperature Water BathChangzhou Jinnan Instrument Manufacturing Co., Ltd.HH - W600
Destaining ShakerWuhan Lingsi Biotechnology Co., Ltd.TSY-B
Dewatering machineWuhan Junjie Electronics Co., Ltd.JT-12J
EosinWuhan Lingsi Biotechnology Co., Ltd.E8090
Embedding ParaffinSinopharm Chemical Reagent Co., Ltd.69019361
Ethanol AbsoluteSinopharm Chemical Reagent Co., Ltd.10009218
Embedding MachineWuhan Junjie Electronics Co., Ltd.JB-P5
GraphPad Prism/8.0.2
Glacial Acetic AcidSinopharm Chemical Reagent Co., Ltd.10000218
GeneCards/https://www.genecards.org/
GlycerolSinopharm Chemical Reagent Co., Ltd.10010618
GlycineBiofroxx1275GR500
Gene Ontology (GO)/geneontology.org
HematoxylinWuhan Lingsi Biotechnology Co., Ltd.G1140
Horizontal ShakerHaimen Qilinbeier Instrument Manufacturing Co., Ltd., JiangsuTS - 1
Hydrophobic Barrier PenServicebioWG1066-1
HCIXinyang Chemical Reagent FactoryGB622-89
High-performance liquid chromatographyAgilent1200
IsopropanoSinopharm Chemical Reagent Co., Ltd.80109218
Image-Pro PlusMedia CyberneticsVersion 6.0
Imaging SystemNikonDS-Fi3
IRIS-Fine Micro Dissecting ScissorsRWD Life Science Co., Ltd.S12004-09
Kyoto Encyclopedia of Genes and Genomes (KEGG)/www.genome.jp/kegg/
Microscope SlideNantong Mevid Life Science Co., Ltd.PC2-301
Microwave OvenGalanz Microwave Oven Electrical Appliances Co., Ltd.P70D20TL-P4
Mini CentrifugeDragon LabD1008E
Neutral ResinWuhan Lingsi Biotechnology Co., Ltd.G8590
NaClSinopharm Chemical Reagent Co., Ltd.10019318
NCBI/www.ncbi.nlm.nih.gov
Non-contact ultrasonic cell pulverizerSCIENTZ08-III
n-ButanolSinopharm Chemical Reagent Co., Ltd.100052190
OvenShanghai Huitai Instrument Manufacturing Co., Ltd.DHG-9140A
PubChem/https://pubchem.ncbi.nlm.nih.gov/
PBS Phosphate Buffer PowderWuhan Lingsi Biotechnology Co., Ltd.P1010
Pathological MicrotomeLeica Instruments Co., Ltd.(Shanghai)RM2016
Portable CentrifugeWuhan Lingsi Biotechnology Co., Ltd.D1008
R 4.5.1//
RIPA Lysis Buffer (1.5 ML)MeilunbioMA0151
Sodium dodecyl sulfate (SDS)Sigma-AldrichL3771-500G
SpeedVacGemmyCV200
SPSS softwareIBM SPSS statisticsversion 22
Suture Tying ForcepsRWD Life Science Co., Ltd.FC11001R-11
Stereo Dissecting MicroscopeRWD Life Science Co., Ltd.77001S
Spin Columns RMCWBIOCW0597S
timsTOF Pro mass spectrometerBruker DaltonicstimsTOF Pro
Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP)/https://tcmspw.com/tcmsp.php
Thermo mixerHangzhou Miu InstrumentsMTC-100
Trifluoroacetic acid (TFA)Sigma-Aldrich299537-500G
Tris(2-carboxyethyl)phosphine hydrochloride (TCEP)Sigma-AldrichC4706-10G
TrypsinSigma-AldrichT575-31N-10
Tissue HomogenizerHangzhou Miu InstrumentsMT-30K
Tris(hydroxymethyl)aminomethane (Tris base)Sigma-AldrichT1503-1KG
UltiMate 3000 RSLCnano systemThermo ScientificUltiMate 3000 RSLCnano
UniProt (2023-01-03)The Francis Crick Institutegithub.com/vdemichev/DiaNN
UreaSigma-Aldrich51456-500G
Venny2.1/https://www.bioinformatics.com.cn/static/others/jvenn/example.html
Vortex MixerKylin-BellVORTEX-5
Vannas Spring-Loaded ScissorsRWD Life Science Co., Ltd.S11001-08
Water (LC-MS)Thermo Fisher ScientificW6-4
XyleneSinopharm Chemical Reagent Co., Ltd.10023418

References

  1. Mensah, G. A., Roth, G. A., Fuster, V. The global burden of cardiovascular diseases and risk factors: 2020 and beyond. J Am Coll Cardiol. 74 (20), 2529-2532 (2019).
  2. Sampson, U. K., Fazio, S., Linton, M. F. Residual cardiovascular risk despite optimal LDL cholesterol reduction with statins: The evidence, etiology, and therapeutic challenges. Curr Atheroscler Rep. 14 (1), 1-10 (2012).
  3. Katzmann, J. L., Packard, C. J., Chapman, M. J., Katzmann, I., Laufs, U. Targeting rna with antisense oligonucleotides and small interfering RNA: JACC state-of-the-art review. J Am Coll Cardiol. 76 (5), 563-579 (2020).
  4. Ward, N. C., Watts, G. F., Eckel, R. H. Statin toxicity. Circ Res. 124 (2), 328-350 (2019).
  5. Xie, T., Guo, J. The effect of the San Jie Tong Mai formula on elderly patients with carotid arteriosclerosis. Chinese Journal of Gerontology. 38 (22), in Chinese 5395-5397 (2018).
  6. Li, P., et al. San Jie Tong Mai Fang protects against atherosclerosis progression by regulating macroautophagy through the PI3K/AKT/mTOR signaling pathway. J Cardiovasc Pharmacol. 82 (4), 333-343 (2023).
  7. Han, H., et al. Mechanism of san jie tong mai fang in atherosclerosis attenuation by JAK/STAT-mediated inhibition of macrophage M1 polarization. J Vis Exp. (221), e68546(2025).
  8. Hopkins, A. L. Network pharmacology. Nat Biotechnol. 25 (10), 1110-1111 (2007).
  9. Hopkins, A. L. Network pharmacology: The next paradigm in drug discovery. Nat Chem Biol. 4 (11), 682-690 (2008).
  10. Vogel, C., Marcotte, E. M. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 13 (4), 227-232 (2012).
  11. Pinzi, L., Rastelli, G. Molecular docking: Shifting paradigms in drug discovery. Int J Mol Sci. 20 (18), (2019).
  12. Huang, T., et al. Approaches in studying the pharmacology of Chinese medicine formulas: Bottom-up, top-down-and meeting in the middle. Chin Med. 13, 15(2018).
  13. Zhao, P., et al. Integration of transcriptomics, proteomics, metabolomics, and systems pharmacology data to reveal the therapeutic mechanism underlying the Chinese herbal BuFei Yishen formula for the treatment of chronic obstructive pulmonary disease. Mol Med Rep. 17 (4), 5247-5257 (2018).
  14. Geng, X., et al. Determining the mechanism of action of the qishan formula against lung adenocarcinoma by integration of network pharmacology, molecular docking, and proteomics. Medicine (Baltimore). 102 (13), e33384(2023).
  15. Ru, J., et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 6, 13(2014).
  16. Li, H., et al. A network pharmacology approach to determine active compounds and action mechanisms of ge-gen-qin-lian decoction for treatment of type 2 diabetes. Evid Based Complement Alternat Med. 2014, 495840(2014).
  17. El Omari, N., et al. Unveiling the molecular mechanisms: Dietary phytosterols as guardians against cardiovascular diseases. Nat Prod Bioprospect. 14 (1), 27(2024).
  18. Feng, S., et al. Intake of stigmasterol and β-sitosterol alters lipid metabolism and alleviates NAFLD in mice fed a high-fat western-style diet. Biochim Biophys Acta Mol Cell Biol Lipids. 1863 (10), 1274-1284 (2018).
  19. Bodet, C., La, V. D., Epifano, F., Grenier, D. Naringenin has anti-inflammatory properties in macrophage and ex vivo human whole-blood models. J Periodontal Res. 43 (4), 400-407 (2008).
  20. Chtourou, Y., Slima, A. B., Makni, M., Gdoura, R., Fetoui, H. Naringenin protects cardiac hypercholesterolemia-induced oxidative stress and subsequent necroptosis in rats. Pharmacol Rep. 67 (6), 1090-1097 (2015).
  21. Luo, Y., Shang, P., Li, D. Luteolin: A flavonoid that has multiple cardio-protective effects and its molecular mechanisms. Front Pharmacol. 8, 692(2017).
  22. Luo, Y., et al. Isorhamnetin attenuates atherosclerosis by inhibiting macrophage apoptosis via PI3K/Akt activation and HO-1 induction. PLoS One. 10 (3), e0120259(2015).
  23. Xu, S., et al. Exploring the mechanisms of guizhifuling pills in the treatment of coronary spastic angina based on network pharmacology combined with molecular docking. Medicine (Baltimore). 103 (29), e39014(2024).
  24. Khalaf, K., et al. Cellular aspects of immunity involved in the development of atherosclerosis. Front Immunol. 16, 1461535(2025).
  25. Kadowaki, T. Insights into insulin resistance and type 2 diabetes from knockout mouse models. J Clin Invest. 106 (4), 459-465 (2000).
  26. Ginsberg, H. N. Insulin resistance and cardiovascular disease. J Clin Invest. 106 (4), 453-458 (2000).
  27. Taskinen, M. R. Diabetic dyslipidaemia: From basic research to clinical practice. Diabetologia. 46 (6), 733-749 (2003).
  28. Kamagate, A., et al. Foxo1 mediates insulin-dependent regulation of hepatic VLDL production in mice. J Clin Invest. 118 (6), 2347-2364 (2008).
  29. Mulvihill, E. E., et al. Nobiletin attenuates VLDL overproduction, dyslipidemia, and atherosclerosis in mice with diet-induced insulin resistance. Diabetes. 60 (5), 1446-1457 (2011).
  30. Rask-Madsen, C., King, G. L. Mechanisms of disease: Endothelial dysfunction in insulin resistance and diabetes. Nat Clin Pract Endocrinol Metab. 3 (1), 46-56 (2007).
  31. Han, S., et al. Macrophage insulin receptor deficiency increases ER stress-induced apoptosis and necrotic core formation in advanced atherosclerotic lesions. Cell Metab. 3 (4), 257-266 (2006).
  32. Rask-Madsen, C., et al. Loss of insulin signaling in vascular endothelial cells accelerates atherosclerosis in apolipoprotein E null mice. Cell Metab. 11 (5), 379-389 (2010).
  33. Braverman, J., Stanley, S. A. Nitric oxide modulates macrophage responses to mycobacterium tuberculosis infection through activation of hif-1α and repression of nf-κb. J Immunol. 199 (5), 1805-1816 (2017).
  34. Cao, C., et al. Ikkε knockout prevents high-fat diet-induced arterial atherosclerosis and NF-κB signaling in mice. PLoS One. 8 (5), e64930(2013).
  35. Gliozzi, M., et al. Modulation of nitric oxide synthases by oxidized LDLs: Role in vascular inflammation and atherosclerosis development. Int J Mol Sci. 20 (13), (2019).
  36. Chantong, B., Kratschmar, D. V., Nashev, L. G., Balazs, Z., Odermatt, A. Mineralocorticoid and glucocorticoid receptors differentially regulate NF-κB activity and pro-inflammatory cytokine production in murine BV-2 microglial cells. J Neuroinflammation. 9, 260(2012).
  37. Rask, E., et al. Tissue-specific dysregulation of cortisol metabolism in human obesity. J Clin Endocrinol Metab. 86 (3), 1418-1421 (2001).
  38. Livingstone, D. E., et al. Understanding the role of glucocorticoids in obesity: Tissue-specific alterations of corticosterone metabolism in obese Zucker rats. Endocrinology. 141 (2), 560-563 (2000).
  39. Kipari, T., et al. 11β-hydroxysteroid dehydrogenase type 1 deficiency in bone marrow-derived cells reduces atherosclerosis. Faseb J. 27 (4), 1519-1531 (2013).
  40. Charo, I. F., Taubman, M. B. Chemokines in the pathogenesis of vascular disease. Circ Res. 95 (9), 858-866 (2004).
  41. Hadoke, P. W., Kipari, T., Seckl, J. R., Chapman, K. E. Modulation of 11β-hydroxysteroid dehydrogenase as a strategy to reduce vascular inflammation. Curr Atheroscler Rep. 15 (5), 320(2013).
  42. Allerheiligen, S. R. Next-generation model-based drug discovery and development: Quantitative and systems pharmacology. Clin Pharmacol Ther. 88 (1), 135-137 (2010).
  43. Fleisher, B., Brown, A. N., Ait-Oudhia, S. Application of pharmacometrics and quantitative systems pharmacology to cancer therapy: The example of luminal a breast cancer. Pharmacol Res. 124, 20-33 (2017).
  44. Leil, T. A., Ermakov, S. Editorial: The emerging discipline of quantitative systems pharmacology. Front Pharmacol. 6, 129(2015).
  45. Zha, X., Ji, R., Li, Y., Cao, R., Zhou, S. Network pharmacology, molecular docking, and molecular dynamics simulation analysis reveal the molecular mechanism of halociline against gastric cancer. Mol Divers. 29 (5), 4693-4703 (2025).

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Atherosclerosis MechanismProteomics AnalysisApoE Knockout MiceBioactive ComponentsMolecular DockingGlucocorticoid MetabolismVascular InflammationHsd11b1 Target
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