Integrated Network Pharmacology and Metabolomics Analysis of the Therapeutic Effects of Zi Dian Fang on Immune Thrombocytopenic Purpura

Li, Yubo; Li, Yamei; Lu, Wenliang; Li, Hongbin; Wang, Yuming; Luo, Houmin; Dong, Wenying; Bai, Gang; Zhang, Yanjun

doi:10.3389/fphar.2018.00597

Cited by 33 publications

(26 citation statements)

References 66 publications

(63 reference statements)

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“…DAVID tools were able to identify enriched biological themes, particularly GO terms that involved biological process (BP), cell component (CC), and molecular functions (MFs) and visualized genes on KEGG pathway maps [ 20 ]. The target of EGb acting on aging was inputted into the DAVID database with the organism limited to “Homo sapiens.” The dividing value of recognized GO terms was set to FDR < 0.05, and KEGG pathways were set to P < 0.05 [ 21 , 22 ].…”

Section: Methodsmentioning

confidence: 99%

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Network Pharmacology‐Based Strategy to Investigate the Pharmacological Mechanisms of Ginkgo biloba Extract for Aging

Liu

Zhang

et al. 2020

Evidence-Based Complementary and Alternative Medicine

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Aging is a main risk factor for a number of debilitating diseases and contributes to an increase in mortality. Previous studies have shown that Ginkgo biloba extract (EGb) can prevent and treat aging-related diseases, but its pharmacological effects need to be further clarified. This study aimed to propose a network pharmacology-based method to identify the therapeutic pathways of EGb for aging. The active components of EGb and targets of sample chemicals were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database. Information on aging-related genes was obtained from the Human Ageing Genomic Resources database and JenAge Ageing Factor Database. Subsequently, a network containing the interactions between the putative targets of EGb and known therapeutic targets of aging was established, which was used to investigate the pharmacological mechanisms of EGb for aging. A total of 24 active components, 154 targets of active components of EGb, and 308 targets of aging were obtained. Network construction and pathway enrichment were conducted after data integration. The study found that flavonoids (quercetin, luteolin, and kaempferol) and beta-sitosterol may be the main active components of EGb. The top eight candidate targets, namely, PTGS2, PPARG, DPP4, GSK3B, CCNA2, AR, MAPK14, and ESR1, were selected as the main therapeutic targets of EGb. Pathway enrichment results in various pathways were associated with inhibition of oxidative stress, inhibition of inflammation, amelioration of insulin resistance, and regulation of cellular biological processes. Molecular docking results showed that PPARG had better binding capacity with beta-sitosterol, and PTGS2 had better binding capacity with kaempferol and quercetin. The main components of EGb may act on multiple targets, such as PTGS2, PPARG, DPP4, and GSK3B, to regulate multiple pathways, and play an antiaging role by inhibiting oxidative stress, inhibiting inflammation, and ameliorating insulin resistance.

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Section: Methodsmentioning

confidence: 99%

“…e target of EGb acting on aging was inputted into the DAVID database with the organism limited to "Homo sapiens." e dividing value of recognized GO terms was set to FDR < 0.05, and KEGG pathways were set to P < 0.05 [21,22].…”

Section: Gene Ontology (Go) and Pathway Enrichment Analysismentioning

confidence: 99%

Network Pharmacology‐Based Strategy to Investigate the Pharmacological Mechanisms of Ginkgo biloba Extract for Aging

Liu

Zhang

et al. 2020

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

show abstract

“…The target of EGb acting on aging was inputted into DAVID database with the organism limited to "Homo sapiens". The dividing value of recognized GO terms were set to FDR < 0.05 and KEGG pathways were set to P value < 0.05 [21,22].…”

Section: Gene Ontology and Pathway Enrichment Analysismentioning

confidence: 99%

Network pharmacology-based strategy to investigate pharmacological mechanisms of Ginkgo Biloba Extract for Aging

Liu

Zhang

et al. 2020

Preprint

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Background Aging represents the main risk factor for a number of debilitating diseases and contributes to increase in mortality. Previous studies have shown that ginkgo biloba extract (EGb) can prevent and treat aging-related diseases, but its pharmacological effects need to be further clarified. In this study, we proposed a network pharmacology-based method to identify the therapeutic pathways of EGb for aging.Methods The active components of EGb and targets of sample chemicals were obtained from TCMSP database. Aging-related genes were obtained by retrieving the Human Ageing Genomic Resources database and the JenAge Ageing Factor Database. Then, a network containing the interactions between the putative targets of EGb and known therapeutic targets of aging was built, which was used to investigate pharmacological mechanisms of EGb for Aging.Results 24 active components, 154 targets of active components of EGb and 308 targets of aging were obtained. Network construction and pathway enrichment were carried out after data integration. The research found that flavonoids (quercetin, luteolin, kaempferol) and beta-sitosterol might be the main active component of EGb. The top eight candidate targets, including PTGS2, PPARG, DPP4, GSK3B, CCNA2, AR, MAPK14, ESR1, were chosen as EGb’s main therapeutic targets. The results of pathway enrichment participated in various pathways associated with inhibiting oxidative stress, inhibiting inflammation, ameliorating insulin resistance and regulating cellular biological processes, etc. Molecular docking results showed that PPARG had better binding capacity with beta-sitosterol and PTGS2 had better binding capacity with kaempferol and quercetin.Conclusions The main components of EGb might act on multiple targets, such as PTGS2, PPARG, DPP4, GSK3B, etc., to regulate multiple pathways and played an anti- aging role by inhibiting oxidative stress, inhibiting inflammation, ameliorating insulin resistance.

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“…Previous studies using high-throughput microarray platforms have facilitated knowledge discovery and are increasingly valued for identifying the initiation and progression of complex diseases from multiple scales (16,17). With the continuous progression of the systems biology research field, network pharmacology is becoming a novel subject for scientific researchers to investigate and identify interconnecting disease features, bioactive agents and drug targets in complex biological systems (16)(17)(18), as well as bioinformatics, an interdisciplinary field that has key roles in the analysis of high-throughput data (19). Microarray analysis of T2D, combining network pharmacology with bioinformatics methods, provides a promising strategy to identify molecular biological information, and are also powerful tools for the development of anti-T2D targets (19).…”

Section: In Silico Prediction and Validation Of Potential Therapeuticmentioning

confidence: 99%

<em>In silico</em> prediction and validation of potential therapeutic genes in pancreatic β‑cells associated with type 2 diabetes

Zhou¹,

Mou²,

Liu³

et al. 2020

Exp Ther Med

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Diabetes mellitus is becoming a major health burden worldwide. Pancreatic β-cell death is a characteristic of type 2 diabetes (T2D), but the underlying mechanisms of pancreatic β-cell death remain unknown. Therefore, the aim of the present study was to identify potential targets in the pancreatic islet of T2D. The GSE20966 dataset was obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by using the GEO2R tool. The Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes Pathway enrichment analysis of DEGs were further assessed using the Database for Annotation, Visualization and Integrated Discovery. Furthermore, protein-protein interaction (PPI) networks were constructed for the up-and downregulated genes using STRING databases and were then visualized with Cytoscape. The body weight, fasting blood glucose (FBG), pancreatic index and biochemistry parameters were measured in db/db mice. Moreover, the morphology of the pancreas was detected by hematoxylin and eosin staining, and hub genes were assessed using reverse transcription-quantitative PCR (RT-qPCR) and western blot analysis. In total, 570 DEGs were screened, including 376 upregulated and 194 downregulated genes, which were associated with 'complement activation, classical pathway', 'proteolysis', 'complement activation' and 'pancreatic secretion pathway'. It was found that the body weight, FBG, alanine aminotransferase, aspartate aminotransferase, total cholesterol, triglycerides, blood urea nitrogen, creatinine, fasting serum insulin, glucagon and low-density lipoprotein cholesterol levels were significantly higher in db/db mice, while high-density lipoprotein cholesterol levels and the pancreatic index were significantly decreased. Furthermore, albumin, interleukin-8, CD44, CC motif chemokine ligand 2, hepatocyte growth factor, cystic fibrosis transmembrane conductance regulator, histone cluster 1 H2B family member n, mitogen-activated protein kinase 11 and neurotrophic receptor tyrosine kinase 2 were identified as hub genes in PPI network. RT-qPCR and western blotting results demonstrated the same expression trend in hub genes as found by the bioinformatics analysis. Therefore, the present study identified a series of hub genes involved in the progression of pancreatic β-cell, which may help to develop effective therapeutic strategy for T2D.

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Integrated Network Pharmacology and Metabolomics Analysis of the Therapeutic Effects of Zi Dian Fang on Immune Thrombocytopenic Purpura

Cited by 33 publications

References 66 publications

Network Pharmacology‐Based Strategy to Investigate the Pharmacological Mechanisms of Ginkgo biloba Extract for Aging

Network Pharmacology‐Based Strategy to Investigate the Pharmacological Mechanisms of Ginkgo biloba Extract for Aging

Network pharmacology-based strategy to investigate pharmacological mechanisms of Ginkgo Biloba Extract for Aging

<em>In silico</em> prediction and validation of potential therapeutic genes in pancreatic β‑cells associated with type 2 diabetes

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Integrated Network Pharmacology and Metabolomics Analysis of the Therapeutic Effects of Zi Dian Fang on Immune Thrombocytopenic Purpura

Cited by 33 publications

References 66 publications

Network Pharmacology‐Based Strategy to Investigate the Pharmacological Mechanisms of Ginkgo biloba Extract for Aging

Network Pharmacology‐Based Strategy to Investigate the Pharmacological Mechanisms of Ginkgo biloba Extract for Aging

Network pharmacology-based strategy to investigate pharmacological mechanisms of Ginkgo Biloba Extract for Aging

<em>In&nbsp;silico</em> prediction and validation of potential therapeutic genes in pancreatic &beta;‑cells associated with type 2 diabetes

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<em>In silico</em> prediction and validation of potential therapeutic genes in pancreatic β‑cells associated with type 2 diabetes