Objectives: By the ways of Network Pharmacology and Molecular Docking Technology,researching the potential targets and the mechanism of action of proanthocyanidins combined with Allicin against atherosclerosis(AS). Methods: Searching in the Genecards database, collect the targets of Proanthocyanidins, Allicin and AS and try to analyse the common targets of them. Import the common targets of Proanthocyanidins, Allicin and AS into the web platform called Venny2.1.0 and get the Venn diagram; then after importing the intersecting targets that we got into the STRING database to build up the PPI net, beautify the PPT net by the Cytoscape software which could lead to the target nets of “Medicine and Disease.” Import the targets of how the medicines is used to treat the AS which is analyzed before into the web platform Metascape to perform GO and KEGG enrichment of analysismedicines and diseases. Make use of vina, pymol, ligplot and GROMACS software to perform the Docking validation and Molecular dynamics studies about the affinities among the medicine ingredient targets and core AS targets. Results: The key targets of Proanthocyanidins combined with Allicin against the relevant proteins of AS include Jun proto-oncogene(JUN), epidermal growth factor receptor(EGFR), mitogen-activated protein kinase 3(MAPK3), tumor necrosis factor (TNF), interleukin 6(IL6) and vascular endothelial growth factor A (VEGFA), etc. According to the enrichment analyses of GO, we find 2 pathways: GO:0000302 response to reactive oxygen species and GO:0009725:response to hormone; According to the enrichment analyses of KEGG, we find that AS closely relates to cancer, Lipids and Fluid shear stress. Docking validation and Molecular dynamics studies show that Proanthocyanidins combined with Allicin are capable to stably combine with eNOS and TNF-α. Conclusion: Proanthocyanidins combined with Allicin can fight against AS by IL-6, TNF, VEGF and JUN and other core targets. Meanwhile, by the pathways of Cancers, Lipids and Fluid shear stress.
Bipolar disorder (BD), which also refers to manic-depressive disease, often synchronously develops accompanying mental disorder, endocrine, and long-term awful physical conditions and has a poor prognosis, making bipolar disorder a significant public health problem and a global disease burden. The disorder is highly heritable and has been shown to be genetically linked. Identifying and screening major molecular markers for therapeutic targeting has become a global concern. This paper aims to use a bioinformatics approach to screen genes included in the pathogenesis of bipolar affective disorder and to analyze their signaling pathways. In this study, mRNA expression profiles in the orbitofrontal cortex (GSE5389) and dorsolateral brain tissue (GSE5388) obtained from the GEO public database on the National Center for Biological Information (NCBI) website were employed in order to detect and analyze differentially expressed genes (DEGs) in the normal and disease groups adopting the limma package with the R language, followed by an application of Metascape database for GO and KEGG signaling pathway analysis and WGCNA package for GSE5389 for weighted gene co-expression network analysis in order to acquire the relevant modules as well as crucial genes relation. It was shown that patients with bipolar disorder, genes included in the development of neuronal cell development, cell agglutination, immune signaling, and glial cell regeneration are dysregulated.
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