中国组织工程研究2025，Vol.29Issue(35)：7552-7561,10.DOI:10.12307/2025.968
基于人工智能和组学数据驱动的中药潜在机制新型分析预测方法
A novel analysis and prediction method for potential mechanisms of traditional Chinese medicine based on artificial intelligence and omics data-driven approach
摘要
Abstract
BACKGROUND:The treatment of diseases with traditional Chinese medicine is a complex multi-target regulatory process.It is of great significance to explore the multi-target integration effect of traditional Chinese medicine by combining technologies from multiple fields such as artificial intelligence,single-cell transcriptomics,spatial transcriptomics,and bioinformatics. OBJECTIVE:To propose a novel analytical prediction method for potential mechanisms of traditional Chinese medicines,which is different from network pharmacology,based on artificial intelligence and omics data driven,with an example of exploring the potential mechanisms of Dachaihu Decoction for treating hyperlipidemia and atherosclerosis. METHODS:(1)The pharmacodynamic protein targets of the constituent drugs of Dachaihu Decoction were collected through TCMSP database,and the disease targets of hyperlipidemia were obtained in Genecards,NCBI,and TTD.(2)The single-cell transcriptome samples of hyperlipidemia(the first set of single-cell data samples from aortic valves of wild-type,Apoe knockout,and Ldlr knockout mice;the second set of single-cell data samples from Ldlr knockout mice fed with high cholesterol versus normal feeding)and spatial transcriptome samples from human coronary atherosclerosis tissue sections were obtained from the GEO database.A deep neural network autoencoder model was developed to encode the transcriptome sequencing data,and the integrated coded values(MTIS)were mapped to the single-cell level and spatial organization level using single-cell transcriptome and spatial transcriptome technologies for comparative statistical analyses of the samples and identification of the main effector cells and effector genes. RESULTS AND CONCLUSION:(1)There were significant differences in the data morphology and statistics of MTIS between wildtype and Apoe-knockout mice treated with Dachaihu Decoction(P＜0.000 1),as well as between wildtype and Ldlr-knockout mice treated with Dachaihu Decoction(P＜0.000 1).(2)The main effector cells of Dachaihu Decoction in Apoe-knockout mice were aortic valve stromal cells,while the main effector cells in Ldlr-knockout mice were white blood cells,fibroblasts,and vascular endothelial cells.Except for Ldlr and Apoe,the main effector genes are Vcam1,Fn1,and Mmp2.(3)There were statistically significant differences(P＜0.000 1)in MTIS between high cholesterol fed samples and normal fed samples of Ldlr-knockout mice treated with Dachaihu Decoction.The main effector cells were macrophages,and the main effector genes were Fn1,F7,Ptgs1,IL6 and App.(4)The spatial transcriptome comparisons of MTIS in human coronary artery slices showed that high MTIS value cells appeared to be distributed in both blood vessels and atherosclerotic plaque areas,while low MTIS value cells appeared to be mainly concentrated in the endothelial cells and atherosclerotic plaque areas.To conclude,this new analytical method achieves quantitative analysis of the multi-target integration effects of traditional Chinese medicine at the single-cell level and organ spatial tissue level,which is used to explore the potential mechanism of Dachaihu Decoction in treating hyperlipidemia and atherosclerosis.关键词
人工智能/单细胞转录组/空间转录组/生物信息学/网络药理学/多靶点/中药/基因敲除/组学/药理Key words
artificial intelligence/single cell transcriptome/spatial transcriptome/bioinformatics/network pharmacology/multi-target/traditional Chinese medicine/knockout/omics/pharmacology分类
医药卫生引用本文复制引用
江启煜,曾慧妍..基于人工智能和组学数据驱动的中药潜在机制新型分析预测方法[J].中国组织工程研究,2025,29(35):7552-7561,10.基金项目
国家自然科学基金项目(82374233),项目负责人:曾慧妍 （82374233）
广东省自然科学基金(2414050003181),项目负责人:曾慧妍 National Natural Science Foundation of China,No.82374233(to ZHY) （2414050003181）
Natural Science Foundation of Guangdong Province,No.2414050003181(to ZHY) （to ZHY）
