Mechanisms of QingRe HuoXue Formula in atherosclerosis Treatment: An integrated approach using Bioinformatics, Machine Learning, and experimental validation.

BACKGROUND: Atherosclerosis (AS) is the main cause of coronary heart disease, cerebral infarction, and peripheral vascular disease. QingRe HuoXue Formula (QRHXF), a common prescription of traditional Chinese medicine, has a definite effect on the clinical treatment of AS, but its mechanism remains to be further explored. PURPOSE: The current study aimed to demonstrate the effectiveness of the QRHXF in the treatment of AS and further reveal its potential pharmacological mechanisms. METHODS: Explore the potential mechanisms of QRHXF in treating AS through network pharmacology, machine learning, transcriptome analysis, and molecular docking, then validate them through animal experiments and PCR experiments. RESULTS: The results indicate that through network pharmacology and machine learning methods, 10 genes including COL1A1 and CCR7 have been identified as potential candidate genes for QRHXF treatment of atherosclerosis. Molecular docking indicates that the key active compounds of QRHXF have good binding affinity with the predicted genes. Two key genes, COL1A1 and CCR7, were identified through transcriptome sequencing analysis of the aortic tissue of APOE-/- mice in the AS model. Finally, the animal and PCR experiment found that QRHXF can effectively reduce the formation of aortic plaques in APOE-/- mice of the AS model, lower blood lipid levels in mice, and upregulate the mRNA expression level of COL1A1, promoting the formation of fibrosis within plaques. CONCLUSIONS: We revealed the inflammatory and immune pathways underlying QRHXF treatment for AS, and verified through transcriptome sequencing and experiments that QRHXF can promote the expression of COL1A1, thereby increasing the stability of AS plaques.

Huanglian Jiedu Decoction enhances the stability of atherosclerotic plaques through SLC2A1-mediated efferocytosis.

BACKGROUND: Atherosclerotic (AS) plaques require a dense necrotic core and a robust fibrous cap to maintain stability. While previous studies have indicated that the traditional Chinese medicine Huang Lian Jie Du Decoction (HLJDD) possesses the capability to stabilize AS plaques, the underlying mechanisms remain obscure. This study aims to delve deeper into the potential mechanisms by which HLJDD improves AS through an integrated research strategy. METHODS: Leveraging an AS model in ApoE-/- mice exposed to a high-fat diet (HFD), we scrutinized the therapeutic effects of HLJDD using microscopic observations, oil red O staining, HE staining and Masson staining. Employing comprehensive techniques of network pharmacology, bioinformatics, and molecular docking, we elucidated the mechanism by which HLJDD stabilizes AS plaques. In vitro experiments, utilizing ox-LDL-induced macrophages and apoptotic vascular smooth muscle cells (VSMCs), assessed the impact of HLJDD on efferocytosis and the role of SLC2A1. RESULTS: In vivo experiments showcased the efficacy of HLJDD in reducing the quantity of aortic plaques, diminishing lipid deposition, and enhancing plaque stability in AS mice. Employing network pharmacology and machine learning, we pinpointed SLC2A1 as a crucial regulatory target. Molecular docking further validated the binding of HLJDD components with SLC2A1. The experiments demonstrated a dose-dependent upregulation in SLC2A1 expression by HLJDD, amplifying efferocytosis. Importantly, this effect was reversed by the SLC2A1 inhibitor STF-31, highlighting the pivotal role of SLC2A1 as a target. CONCLUSION: The HLJDD can modulate macrophage efferocytosis by enhancing the expression levels of SLC2A1, thereby improving the stability of atherosclerotic plaques.

Screening for immune-related biomarkers associated with myasthenia gravis and dilated cardiomyopathy based on bioinformatics analysis and machine learning.

Background: We aim to investigate genes associated with myasthenia gravis (MG), specifically those potentially implicated in the pathogenesis of dilated cardiomyopathy (DCM). Additionally, we seek to identify potential biomarkers for diagnosing myasthenia gravis co-occurring with DCM. Methods: We obtained two expression profiling datasets related to DCM and MG from the Gene Expression Omnibus (GEO). Subsequently, we conducted differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) on these datasets. The genes exhibiting differential expression common to both DCM and MG were employed for protein-protein interaction (PPI), Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Additionally, machine learning techniques were employed to identify potential biomarkers and develop a diagnostic nomogram for predicting MG-associated DCM. Subsequently, the machine learning results underwent validation using an external dataset. Finally, gene set enrichment analysis (GSEA) and machine algorithm analysis were conducted on pivotal model genes to further elucidate their potential mechanisms in MG-associated DCM. Results: In our analysis of both DCM and MG datasets, we identified 2641 critical module genes and 11 differentially expressed genes shared between the two conditions. Enrichment analysis disclosed that these 11 genes primarily pertain to inflammation and immune regulation. Connectivity map (CMAP) analysis pinpointed SB-216763 as a potential drug for DCM treatment. The results from machine learning indicated the substantial diagnostic value of midline 1 interacting protein1 (MID1IP1) and PI3K-interacting protein 1 (PIK3IP1) in MG-associated DCM. These two hub genes were chosen as candidate biomarkers and employed to formulate a diagnostic nomogram with optimal diagnostic performance through machine learning. Simultaneously, single-gene GSEA results and immune cell infiltration analysis unveiled immune dysregulation in both DCM and MG, with MID1IP1 and PIK3IP1 showing significant associations with invasive immune cells. Conclusion: We have elucidated the inflammatory and immune pathways associated with MG-related DCM and formulated a diagnostic nomogram for DCM utilizing MID1IP1/PIK3IP1. This contribution offers novel insights for prospective diagnostic approaches and therapeutic interventions in the context of MG coexisting with DCM.