Abstract

Objective: The Cistanche-based hypoglycemic-lipid-regulating formula (HLF) has shown significant efficacy in improving disorders of glucose and lipid metabolism. However, its specific mechanisms of action remain unclear. This study aimed to explore the potential targets and mechanisms of HLF in improving glucolipid metabolic disorders through network pharmacology, molecular docking, molecular dynamics (MD) simulations, and experimental validation.

Methods:
Targets associated with glucolipid metabolic disorders were retrieved from the GeneCards database. The action targets of the major active components of HLF were obtained from the CTD and SwissTargetPrediction databases. The STRING database and Cytoscape software were used to construct protein-protein interaction (PPI) networks. After identifying potential key targets, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed via the Metascape database, and a "compound-target-pathway" network was constructed. Molecular docking was conducted using AutoDock and PyMOL to verify the interactions between the main components and core targets. MD simulations were then performed on the best protein-ligand complexes identified from docking. A mouse model of glucolipid metabolic disorder was established for in vivo validation of the network pharmacology findings.

Results:
Topological analysis of the PPI network identified 10 core targets: TNF, CTNNB1, AKT1, NF-κB1, ESR1, FN1, TP53, IL-1β, IL-6, and EGFR. GO enrichment showed these targets were mainly involved in signal transduction and transcriptional regulation. KEGG pathway analysis revealed significant association with lipid and atherosclerosis pathways. Molecular docking confirmed favorable binding affinities between HLF's core components (e.g., lovastatin, coptisine, salvianolic acid B) and targets such as AKT1, FN1, and IL-6. MD simulations further demonstrated stable interactions, especially between AKT1 and compounds like lovastatin, coptisine, and salvianolic acid B. Animal experiments indicated that HLF may exert therapeutic effects by downregulating the expression of AKT1, TP53, FN1, and EGFR, suppressing inflammatory factors such as IL-1β and IL-6, and upregulating CTNNB1 and ESR1. These changes led to improvements in blood glucose and lipid levels in the model mice.

Conclusion:
HLF may regulate glucose and lipid metabolism and improve atherosclerotic lesions by targeting lipid and atherosclerosis-related pathways. It offers a comprehensive therapeutic strategy for preventing and treating metabolic disorders and related complications.

Keywords:
Cistanche-based formula; Glucolipid metabolic disorder; Network pharmacology; Molecular docking; Molecular dynamics simulation; Lipid and atherosclerosis pathway

 

 

 

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1. Introduction

Glucolipid metabolic disorders have become a prominent public health concern worldwide. In 2021, the global prevalence of diabetes reached approximately 529 million, with projections indicating over 1.31 billion cases by 2050. In China, recent studies show a diabetes prevalence of 12.4% in 2018, up from 10.9% in 2013. Dyslipidemia is frequently comorbid with diabetes, significantly increasing the risks of cardiovascular and cerebrovascular events, mortality, and disability.

Epidemiological surveys from 20 tertiary hospitals in China show that 67.1% of type 2 diabetes (T2DM) patients have dyslipidemia. The CCMR-3B study reported that 72% of T2DM patients also had dyslipidemia and/or hypertension, doubling their risk of macrovascular and microvascular complications compared to patients with diabetes alone.

Due to the complex pathogenesis of glucolipid metabolic disorders and the low rate of achieving comprehensive metabolic control, effective treatments are limited. The Cistanche-based hypoglycemic-lipid-regulating formula (HLF), developed under the "phlegm turbidity" theory, aims to clear heat, remove turbidity, and restore balance. Composed of eight herbs including Cistanche, Momordica charantia, Aloe vera, Scutellaria baicalensis, Schisandra chinensis, Zingiber officinale, Salvia miltiorrhiza, and Atractylodes macrocephala, HLF targets core mechanisms of metabolic disorders.

Fingerprint analysis using HPLC identified key compounds: mangiferin, coptisine, jatrorrhizine, salvianolic acid B, aloin, berberine, palmatine, and lovastatin. Previous studies confirmed HLF's efficacy in lowering glucose and lipid levels, reducing cardiovascular risk, and improving insulin resistance. A multicenter, randomized, open-label clinical trial involving 450 patients with obesity-related T2DM and dyslipidemia found HLF effectively reduced blood glucose, regulated lipids, lowered body weight, and improved insulin sensitivity.

Further research suggested HLF may modulate gut microbiota (e.g., Faecalibacterium, Blautia) and influence metabolic pathways like fatty acid degradation and glycolysis/gluconeogenesis. Despite its proven clinical efficacy, the precise mechanisms remain unclear, limiting broader application.

Network pharmacology offers a systems-level approach to elucidate disease mechanisms and drug actions by integrating bioinformatics and high-throughput screening. This study utilizes network pharmacology, molecular docking, molecular dynamics, and animal experiments to uncover the multi-target mechanisms of HLF in treating glucolipid metabolic disorders.

2. Materials and Methods

2.1 Instruments

D3024R High-speed refrigerated centrifuge (SCILOGEX)

Y300C electrophoresis apparatus and JY300C electrophoresis tank (Junyi Oriental, Beijing)

ChampGel 5000 imaging system (Saizhi Innovation, Beijing)

SMA-1000 spectrophotometer (Meilin Hengtong, Beijing)

StepOne Plus qRT-PCR system (ABI)

PowerPac universal electrophoresis unit (Bio-Rad)

PB-10 pH meter (Sartorius)

Tanon 5200 chemiluminescence analyzer (Tanon, Shanghai)

2.2 Herbal Medications

HLF formula included:

Cistanche (Cistanches Herba)

Momordica charantia

Scutellaria baicalensis

Aloe vera

Schisandra chinensis

Zingiber officinale

Salvia miltiorrhiza

Atractylodes macrocephala
All herbs were granulated formula extracts (Tianjiang Pharmaceutical, Jiangyin).

Control drug: Metformin hydrochloride tablets (0.5 g per tablet, Bristol-Myers Squibb, Shanghai).

2.3 Reagents and Antibodies

TRIzol reagent (Invitrogen)

HiFiScript cDNA Synthesis Kit (CW2569)

UltraSYBR Mixture (High ROX, CW2602)

BCA Protein Assay Kit

Primary antibodies targeting p-AKT, p-NF-κB p65, AKT, NF-κB p65, FN1, p53, IL-1β, IL-6, EGFR, TNF-α, β-catenin, ESR1, β-actin

Secondary antibodies (HRP-conjugated goat anti-rabbit/mouse IgG)

2.4 Animal Models

Male C57BL/6J mice (7 weeks old) were purchased and housed under standard SPF conditions. All animal procedures were approved by the Ethics Committee of Guang'anmen Hospital (approval no. IACUC-GAMH-2019-002).

2.5 Target Prediction

Active compounds from HLF were queried in CTD and SwissTargetPrediction (probability > 0.1). Disease-related targets for T2DM and dyslipidemia were retrieved from GeneCards (relevance score > 1). Venn diagrams identified overlapping targets for further PPI analysis.

2.6 PPI Network Construction

Overlapping targets were input into STRING (https://www.string-db.org), with medium confidence (score ≥ 0.4). The network was visualized and analyzed in Cytoscape, and core targets identified by degree centrality.

2.7 GO and KEGG Enrichment

Key targets were submitted to Metascape for GO (BP, MF, CC) and KEGG pathway analysis (P < 0.01). Visualization was done using bubble plots.

2.8 Compound-Target-Pathway Network

Cytoscape was used to build a network connecting herbs, compounds, targets, and pathways, illustrating multi-target, multi-pathway interactions.

2.9 Molecular Docking

2D structures of compounds were obtained from PubChem, converted to PDB using Open Babel. Protein structures were downloaded from RCSB PDB, prepared in AutoDockTools, and docked using SailVina. Binding affinity ≤ −4.25 kcal/mol was considered significant. Docking results were visualized with PyMOL.

2.10 Molecular Dynamics Simulation

MD simulations were performed using Amber18 for selected top complexes. Protein: amber14SB; ligand: gaff; water: OPC model. RMSD and RMSF were calculated from 20 ns NPT simulations.

2.11 Animal Grouping and Intervention

Mice were randomized into 4 groups: Normal (N), Model (M), Metformin (MET), and HLF. Except group N, all mice were fed a high-fat diet (HFD) for 6 weeks. HLF was administered at 14 g/kg (crude herb equivalent), MET at 250 mg/kg. After 8 weeks of intervention, blood and liver tissues were collected for analysis.

2.12 qRT-PCR

Total RNA was extracted using TRIzol. cDNA was synthesized using HiFiScript. qPCR was performed using UltraSYBR and StepOne Plus. Relative expression was calculated via 2^−ΔΔCt.

2.13 Western Blot

Proteins were extracted from liver tissue, quantified, separated by SDS-PAGE, and transferred to PVDF membranes. After blocking and incubation with primary and secondary antibodies, signals were visualized by ECL and quantified using ImageJ. β-actin was used for normalization.

2.14 Statistical Analysis

Data were analyzed using SPSS 23.0 and GraphPad Prism 10.1.2. One-way ANOVA and t-tests were used, with P < 0.05 considered statistically significant.