1.广州医科大学附属中医医院,广东 广州 510000
2.广东省中药上市后质量与药效再评价工程技术研究中心 / 广东省热带亚热带植物资源重点实验室 / 中山大学生命科学学院,广东 广州 510275
3.广州中医药大学青蒿研究中心,广东 广州 510006
胡涛(1995年生),女;研究方向:中西医结合治疗代谢综合征疾病; E-mail:2022210620@stu.gzhmu.edu.cn
郭洁文(1968年生),女;研究方向:中西医结合治疗代谢综合征疾病;E-mail:2019620682@gzhmu.edu.cn
纸质出版日期:2024-09-25,
网络出版日期:2024-07-22,
收稿日期:2024-05-11,
录用日期:2024-06-12
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胡涛,王冰钰,苏薇薇等.基于UPLC-Zone TOF-MS/MS联合网络药理学探讨脉复生防治NAFLD作用[J].中山大学学报(自然科学版)(中英文),2024,63(05):73-82.
HU Tao,WANG Bingyu,SU Weiwei,et al.Applying the UPLC-Zone TOF-MS/MS combined network pharmacology to explore the effects of MFS intervention on NAFLD[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(05):73-82.
胡涛,王冰钰,苏薇薇等.基于UPLC-Zone TOF-MS/MS联合网络药理学探讨脉复生防治NAFLD作用[J].中山大学学报(自然科学版)(中英文),2024,63(05):73-82. DOI: 10.13471/j.cnki.acta.snus.ZR20240154.
HU Tao,WANG Bingyu,SU Weiwei,et al.Applying the UPLC-Zone TOF-MS/MS combined network pharmacology to explore the effects of MFS intervention on NAFLD[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(05):73-82. DOI: 10.13471/j.cnki.acta.snus.ZR20240154.
依据脉复生的化学成分,采用网络药理学、分子对接技术、构建NAFLD小鼠模型,探究脉复生防治NAFLD的作用机制。通过UPLC-Zone TOF-MS/MS对脉复生全成分进行检测;借助Swiss生物信息学研究平台获取活性成分和靶点,在OMIM、Disgenet数据库筛选NAFLD疾病靶点,对交集靶点进行PPI分析;在Metascape网站进行GO与KEGG富集分析,对核心靶点与活性成分展开分子对接;采用HFD诱导C57BL/6J小鼠构建NAFLD模型进行核心靶点验证;指认得到130个化学成分,筛选得到54个活性成分与176个可能作用于NAFLD的潜在靶点;根据Degree值对排名前10的成分与靶点进行分子对接实验,对结合能最优的对接模型TNF与Quercetin、IL-17与Asiatic acid、IL-1
β
与Quercetin进行可视化分析;筛选炎症因子和脂肪代谢相关基因进行qPCR测序。富集分析表明脉复生治疗NAFLD与脂肪酸转化、脂质代谢正向调节等生物过程相关,涉及AGE-RAGE、HIF-1、IL-17等信号通路;与对照组相比,模型组血清学检测ALT、TG、LDL-C指标上升并且HDL-C指标下降(
P
<0.05);与模型组相比,脉复生给药组和辛伐他汀组能够降低小鼠体质量、肝脏和脂肪质量占比(
P
<0.05),降低血清中TNF-
α
、IL-1
β
和IL-17的含量和mRNA表达,在高剂量时具有显著性差异(
P
<0.01);与对照组相比,脉复生给药组能够通过调节脂质代谢相关基因的表达,降低肝脏脂质堆积,缓解肝脏脂肪变性。
The mechanism of MaiFuSheng (MFS) on inhib
iting Nonalcoholic Fatty Liver Disease (NAFLD) were investigated based on the exploration of chemical compositions, the techniques of network pharmacology and molecular docking, and the validation using NAFLD mice model. The components of MFS were identified through UPLC-Zeno TOF-MS/MS. The Swiss Bioinformatics Research Platform were applied to obtain active components and potential targets of MFS. The DisGeNet and OMIM databases were used to screen disease targets of NAFLD. The intersection between disease targets and potential targets of MFS were obtained from Venn diagram, which were analyzed by constructing cross-target protein-protein interaction (PPI) network. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were conducted on Metascape database. The molecular docking about core targets and active components of MFS were carried out. Using high-fat diet (HFD)-induced C57BL/6J mice as NAFLD animal model to validate key targets. A total of 130 compounds were recognized, 54 active constituents and 176 potential targets of NAFLD were collected. The molecular docking experiments for the top 10 components with the targets are based on the degree value. We found docking models with the best binding energy for TNF-
α
with Quercetin,IL-17 with Asiatic acid,and IL-1
β
with Quercetin. We applied qPCR technique to detect above inflammation and lipid metabolism related indicators. Enrichment analysis demonstrated that MFS protects NAFLD via biological processes such as fatty acid transformation and positive regulation of lipid metabolism, as well as signaling pathways such as AGE-RAGE,HIF-1,and IL-17. Compared to the control groups,the model group's ALT,TG,and LDL-C indicators increased,whereas HDL-C indicators dropped(
P
<
0.05). MFS and Simvastatin treatment groups significantly reduced body weight,liver weight,and fat weight compared to the model groups(
P
<
0.05). Higher doses resulted in significant reductions in ser
um levels and expression of TNF-
α
,IL-1
β
,and IL-17(
P
<
0.01). Compared to the control groups,the MFS treatment groups can minimize liver lipid accumulation by modulating the expression of lipid metabolism-related genes and improving hepatic fat breakdown.
UPLC-Zone TOF-MS/MS脉复生非酒精性脂肪肝网络药理学实验验证
UPLC-Zone TOF-MS/MSMaiFuShengNAFLDnetwork pharmacologyexperimental validation
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