Potential Molecular Target Prediction and Docking Verification of Hua‐Feng‐Dan in Stroke Based on Network Pharmacology

Abstract

Objective. Hua-Feng-Dan (HFD) is a Chinese medicine for stroke. This study is to predict and verify potential molecular targets and pathways of HFD against stroke using network pharmacology. Methods. The TCMSP database and TCMID were used to search for the active ingredients of HFD, and GeneCards and DrugBank databases were used to search for stroke-related target genes to construct the “component-target-disease” by Cytoscape 3.7.1, which was further filtered by MCODE to build a core network. The STRING database was used to obtain interrelationships by topology and to construct a protein-protein interaction network. GO and KEGG were carried out through DAVID Bioinformatics. Autodock 4.2 was used for molecular docking. BaseSpace was used to correlate target genes with the GEO database. Results. Based on OB ≥ 30% and DL ≥ 0.18, 42 active ingredients were extracted from HFD, and 107 associated targets were obtained. PPI network and Cytoscape analysis identified 22 key targets. GO analysis suggested 51 cellular biological processes, and KEGG suggested that 60 pathways were related to the antistroke mechanism of HFD, with p53, PI3K-Akt, and apoptosis signaling pathways being most important for HFD effects. Molecular docking verified interactions between the core target (CASP8, CASP9, MDM2, CYCS, RELA, and CCND1) and the active ingredients (beta-sitosterol, luteolin, baicalein, and wogonin). The identified gene targets were highly correlated with the GEO biosets, and the stroke-protection effects of Xuesaitong in the database were verified by identified targets. Conclusion. HFD could regulate the symptoms of stroke through signaling pathways with core targets. This work provided a bioinformatic method to clarify the antistroke mechanism of HFD, and the identified core targets could be valuable to evaluate the antistroke effects of traditional Chinese medicines.

Transcriptome Analyses Revealed Adaptive-Responses in Livers of Mice Treated With Hua-Feng-Dan and Its “Guide Drug” Yaomu

“…On the other hand, the downregulation of the PI3K inhibitor LY294002 [47], p38 MAPK inhibitor SB203580 [48], and MEK1/2 inhibitor U0126 [49] further facilitated the activation of these adaptive responses. A network pharmacology analysis of anti-stroke effects of Hua-Feng-Dan revealed potential molecular targets [7], and some of the regulated molecules coincide with the adaptive responses induced by Hua-Feng-Dan.…”

“…Hua-Feng-Dan and its "Guide Drug" Yaomu Hua-Feng-Dan (HFD) and its Guide Drug Yaomu (YM) were provided by Hua-Feng-Dan Pharmaceutical Co. (Guizhou, China). Yaomu is the most important part of the traditional medicine Hua-Feng-Dan [15], and fermented for three months before using as the "Guide Drug" to make Hua-Feng-Dan with other herbs, animal-based products, and minerals as listed in Introduction and reported previously [1,[5][6][7][8].…”

“…Hua-Feng-Dan also contains animal-based products (Moschus berezovskii Flerov (Shexiang); Buthus martensii Karsch (Quanxie), Bombyx mori Linnaeus (Jiangcan), and other minerals (Borax; Borneolum Syntheticum) [1]. Network pharmacology has found β-sitosterol, luteolin, baicalein, wogoni and cinnabar are potential active ingredients [7]. In an attempt to examine the pharmacological basis of this traditional medicine, We have used experimental animal models to demonstrate the protective effects of Hua-Feng-Dan against LPS plus MPTP induced Parkinson's disease (PD) mouse model [6], and LPS plus rotenone-induced PD neurodegeneration rat model [5], and identi ed cinnabar and realgar are two active ingredients in the recipe both in LPS-induced neuroin ammation in rat midbrain neuron-glia co-cultures [8] and in LPS plus rotenone induced neuroin ammation and dopaminergic neuron loss in rats [5].…”

Investigating the Mechanisms of Pollen Typhae in the Treatment of Diabetic Retinopathy Based on Network Pharmacology and Molecular Docking

“…The chemical ingredients of PT were gathered from TCMSP database ( http://old.tcmsp-e.com/tcmsp.php ). In order to select the ingredients, which have better pharmacokinetic properties and oral bioavailability in vivo, the ingredients were filtrated by suggested criterion in TCMSP database, and the ingredients whose drug-likeness (DL) ≥0.18 and oral bioavailability (OB) ≥30% were regarded as putative major ingredients and retained [ 16 ]. Additionally, the SMILES number of ingredients obtained from PubChem database was imported into the SwissTargetPrediction database through Probability ∗ >0 to select the predicted targets.…”

GC-MS Profile of Hua-Feng-Dan and RNA-Seq Analysis of Induced Adaptive Responses in the Liver

“…), animal-based products ( Moschus berezovskii Flerov (Shexiang); Buthus martensii Karsch (Quanxie), Bombyx mori Linnaeus (Jiangcan), and other minerals (Borax; Borneolum Syntheticum) ( Liu et al, 2018 ). Network pharmacology has found that β-sitosterol, luteolin, baicalein, and wogoni are potential active ingredients ( Yang et al, 2020 ), but experimental verification is required. We have demonstrated the protective effects of Hua-Feng-Dan against LPS plus MPTP-induced Parkinson’s disease (PD) mouse model ( Hu et al, 2020 ), and LPS plus rotenone-induced PD rat model ( Chen et al, 2020 ), and identified that cinnabar and realgar are two active ingredients in the recipe both in LPS-induced neuroinflammation in rat midbrain neuron–glia cocultures ( Zhang et al, 2012a ) and in LPS plus rotenone-induced dopaminergic neuron loss in rats ( Chen et al, 2020 ).…”