Network Pharmacology and Single-Cell Transcriptomic Investigation of Molecular Mechanisms Underlying

Background: Acute myocardial infarction (AMI) represents a life-threatening cardiovascular emergency characterized by myocardial necrosis resulting from prolonged ischemia. Methods: Active pharmaceutical constituents of EB and their corresponding molecular targets were systematically retrieved from TCMSP and SwissTargetPrediction databases. AMI-associated genes were compiled from GeneCards, OMIM, and DisGeNET repositories. Protein-protein interaction (PPI) networks were constructed utilizing STRING database, with core targets identified through topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to characterize biological functions. Single-cell RNA sequencing (scRNA-seq) data from myocardial infarction specimens were processed using 10× Genomics platform. Comprehensive analytical workflows included Seurat-based clustering algorithms, UMAP dimensionality reduction, differential expression profiling, functional enrichment characterization, and Monocle 3-mediated pseudotime trajectory reconstruction. Experimental validations encompassed murine AMI models, cellular senescence assays, and molecular interaction studies. Results: Network pharmacology analysis identified 45 bioactive compounds from EB corresponding to 286 potential therapeutic targets. Cross-referencing with AMI-related genes yielded 127 overlapping targets, suggesting multitarget pharmacological interventions. PPI network topology analysis revealed hub genes including VEGFA, AKT1, TNF, IL6, and PTGS2, indicating predicted involvement in angiogenesis, inflammation, and apoptotic pathways. KEGG enrichment demonstrated significant associations with PI3K-AKT, MAPK, and TNF signaling cascades. Single-cell transcriptomic profiling successfully delineated heterogeneous cardiac cell populations comprising cardiomyocytes, cardiac fibroblasts, endothelial cells, macrophages, and T lymphocytes. Differential expression analysis revealed substantial transcriptional reprogramming in post-infarction myocardium, particularly within macrophage and fibroblast populations. Pseudotime trajectory analysis elucidated dynamic cellular state transitions during myocardial repair processes. Integration of network pharmacology predictions with scRNA-seq data demonstrated that predicted EB target genes exhibited preferential expression in inflammation-associated cell types. Functional enrichment of differentially expressed genes highlighted extracellular matrix reorganization, inflammatory response modulation, and neovascularization as principal biological processes potentially targeted by EB intervention. Conclusion: Through systematic integration of network pharmacology and single-cell transcriptomics, this investigation established a predictive computational framework characterizing the multicomponent, multitarget, multipathway mechanisms underlying EB-mediated cardioprotection in AMI. The findings provide computational evidence and hypothesis-generating insights supporting EB therapeutic applications and establish a foundation for future experimental validation and precision medicine approaches in AMI management.