scholarly journals Identification of the Active Constituents and Significant Pathways of Bushen Huoxue Decoction for the Treatment of Recurrent Spontaneous Abortion Based on Network Pharmacology

2020 ◽  
Author(s):  
Meihe LI ◽  
Jinping WU ◽  
Minchao KANG ◽  
Ying XU ◽  
Xin XU ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Chinese Medicine ◽  
Drug Target ◽  
Kegg Pathway ◽  
Cell Model ◽  
Interaction Network ◽  
Receptor Interaction ◽  
Target Interaction ◽  
Target Disease ◽  
Bushen Huoxue Decoction

Abstract Background: To explore Bushen Huoxue Decoction (BHD) mechanism in recurrent spontaneous abortion (RSA).Methods: We predicted and screened the action targets of four Traditional Chinese Medicine representatives by Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, and searched the disease targets of RSA through Drugbank, DisGeNET, and TTD databases. According to the obtained drug targets and disease targets, the "drug target-disease target" interaction network was further analyzed and constructed. The core target of BHD for treating RSA was were imported to establish in STRING. GO and KEGG pathway analysis were conducted with the RStudio (ggplot2). Finally, HTR-8/SVneo cells were selected to establish a cell model of oxidative stress, and the network pharmacologic results of BHD-RSA were verified by cell biology, Western blot and qRT-PCR.Results: A total of 73 active compounds were obtained from 1084 ingredients present in the BHD, and 125 genes were closely related to RSA. According to the "drug target-disease target" interaction network analysis, 26 core targets of BHD for treating RSA were finally selected, and 203 biological processes, 10 molecular functions, and 10 cell components were enriched by GO function enrichment. KEGG pathway enrichment related pathways, a total of 19, are mainly associated with a Neuroactive ligand-receptor interaction pathway. Experimental results show that compared with the oxidative stress cell model of RSA, BHD increases the expression of PTGFR, AGTR1 and OXTR mRNA by upregulating the expression of PTGFR, AGTR1 and OXTR protein, which may interfere with the occurrence and development of RSA through Neuroactive ligand-receptor interaction pathway.Conclusion: Combined with the pathological mechanism of RSA and the absence of specific signaling pathways, we hypothesized that the BHD might play a role in tonifying the kidney, strengthening the bone, promoting blood circulation, and removing blood stasis，it also can virtually relieve the symptoms of RSA, and our network pharmacological analysis lays the foundation for future clinical research.These results may help define the possible roles the BHD plays in RSA through Neuroactive ligand-receptor interaction pathway.

scholarly journals Drug-Target Interaction Network Analysis of Gene-Phenotype Connectivity Maintained by Genistein

2020 ◽  
Vol 27 (12) ◽  
pp. 1678-1687 ◽  
Author(s):  
Baoshan Li ◽  
Yi Jiang ◽  
Jingxin Chu ◽  
Qian Zhou
Keyword(s):  
Network Analysis ◽  
Drug Target ◽  
Interaction Network ◽  
Target Interaction

scholarly journals The Human Kinome Targeted by FDA Approved Multi-Target Drugs and Combination Products: A Comparative Study from the Drug-Target Interaction Network Perspective

PLoS ONE ◽  
2016 ◽  
Vol 11 (11) ◽  
pp. e0165737 ◽  
Author(s):  
Ying Hong Li ◽  
Pan Pan Wang ◽  
Xiao Xu Li ◽  
Chun Yan Yu ◽  
Hong Yang ◽  
...  
Keyword(s):  
Comparative Study ◽  
Drug Target ◽  
Interaction Network ◽  
Target Interaction ◽  
Human Kinome

scholarly journals A landscape for drug-target interactions based on network analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247018
Author(s):  
Edgardo Galan-Vasquez ◽  
Ernesto Perez-Rueda
Keyword(s):  
Drug Target ◽  
Target Genes ◽  
Metabolic Diseases ◽  
Drug Repositioning ◽  
Topological Analysis ◽  
Interaction Network ◽  
Clustering Coefficient ◽  
Connected Components ◽  
Target Interaction ◽  
Target Network

In this work, we performed an analysis of the networks of interactions between drugs and their targets to assess how connected the compounds are. For our purpose, the interactions were downloaded from the DrugBank database, and we considered all drugs approved by the FDA. Based on topological analysis of this interaction network, we obtained information on degree, clustering coefficient, connected components, and centrality of these interactions. We identified that this drug-target interaction network cannot be divided into two disjoint and independent sets, i.e., it is not bipartite. In addition, the connectivity or associations between every pair of nodes identified that the drug-target network is constituted of 165 connected components, where one giant component contains 4376 interactions that represent 89.99% of all the elements. In this regard, the histamine H1 receptor, which belongs to the family of rhodopsin-like G-protein-coupled receptors and is activated by the biogenic amine histamine, was found to be the most important node in the centrality of input-degrees. In the case of centrality of output-degrees, fostamatinib was found to be the most important node, as this drug interacts with 300 different targets, including arachidonate 5-lipoxygenase or ALOX5, expressed on cells primarily involved in regulation of immune responses. The top 10 hubs interacted with 33% of the target genes. Fostamatinib stands out because it is used for the treatment of chronic immune thrombocytopenia in adults. Finally, 187 highly connected sets of nodes, structured in communities, were also identified. Indeed, the largest communities have more than 400 elements and are related to metabolic diseases, psychiatric disorders and cancer. Our results demonstrate the possibilities to explore these compounds and their targets to improve drug repositioning and contend against emergent diseases.

scholarly journals DINIES: drug–target interaction network inference engine based on supervised analysis

2014 ◽  
Vol 42 (W1) ◽  
pp. W39-W45 ◽  
Author(s):  
Yoshihiro Yamanishi ◽  
Masaaki Kotera ◽  
Yuki Moriya ◽  
Ryusuke Sawada ◽  
Minoru Kanehisa ◽  
...  
Keyword(s):  
Drug Target ◽  
Network Inference ◽  
Interaction Network ◽  
Inference Engine ◽  
Target Interaction

scholarly journals Identification of Potential Genetic Biomarkers and Target Genes of Peri-Implantitis Using Bioinformatics Tools

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Xiaogen Zhang ◽  
Zhifa Wang ◽  
Li Hu ◽  
Xiaoqing Shen ◽  
Chundong Liu
Keyword(s):  
Target Genes ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Venn Diagram ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment ◽  
Potential Target ◽  
Genetic Biomarkers

Objectives. To investigate potential genetic biomarkers of peri-implantitis and target genes for the therapy of peri-implantitis by bioinformatics analysis of publicly available data. Methods. The GSE33774 microarray dataset was downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) between peri-implantitis and healthy gingival tissues were identified using the GEO2R tool. GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the DAVID database and the Metascape tool, and the results were expressed as a bubble diagram. The protein-protein interaction network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized using Cytoscape. The hub genes were screened by the cytoHubba plugin of Cytoscape. The potential target genes associated with peri-implantitis were obtained from the DisGeNET database and the Open Targets Platform. The intersecting genes were identified using the Venn diagram web tool. Results. Between the peri-implantitis group and the healthy group, 205 DEGs were investigated including 140 upregulated genes and 65 downregulated genes. These DEGs were mainly enriched in functions such as the immune response, inflammatory response, cell adhesion, receptor activity, and protease binding. The results of KEGG pathway enrichment analysis revealed that DEGs were mainly involved in the cytokine-cytokine receptor interaction, pathways in cancer, and the PI3K-Akt signaling pathway. The intersecting genes, including IL6, TLR4, FN1, IL1β, CXCL8, MMP9, and SPP1, were revealed as potential genetic biomarkers and target genes of peri-implantitis. Conclusions. This study provides supportive evidence that IL6, TLR4, FN1, IL1β, CXCL8, MMP9, and SPP1 might be used as potential target biomarkers for peri-implantitis which may provide further therapeutic potentials for peri-implantitis.

scholarly journals QPowered Compound2DeNovoDrugPropMax –A Novel Programmatic Tool Incorporating Deep Learning and In Silico Methods for Automated In Silico Bio- Activity Discovery for any Compound of Interest

2021 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Rafal Madaj ◽  
Akhil Sanker ◽  
Pavan Preetham Valluri
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
In Silico ◽  
Drug Target ◽  
Drug Targets ◽  
Learning Algorithms ◽  
Interaction Network ◽  
Network Data ◽  
Data Set ◽  
Target Interaction

Network data is composed of nodes and edges. Successful application of machine learning/deep<br>learning algorithms on network data to make node classification and link prediction have been shown<br>in the area of social networks through which highly customized suggestions are offered to social<br>network users. Similarly one can attempt the use of machine learning/deep learning algorithms on<br>biological network data to generate predictions of scientific usefulness. In the presented work,<br>compound-drug target interaction network data set from bindingDB has been used to train deep<br>learning neural network and a multi class classification has been implemented to classify PubChem<br>compound queried by the user into class labels of PBD IDs. This way target interaction prediction for<br>PubChem compounds is carried out using deep learning. The user is required to input the PubChem<br>Compound ID (CID) of the compound the user wishes to gain information about its predicted<br>biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target interaction for<br>the input CID. Further the tool also optimizes the compound of interest of the user toward drug<br>likeness properties through a deep learning based structure optimization with a deep learning based<br>drug likeness optimization protocol. The tool also incorporates a feature to perform automated In<br>Silico modelling for the compounds and the predicted drug targets to uncover their protein-ligand<br>interaction profiles. The program is hosted, supported and maintained at the following GitHub<br><div>repository</div><div><br></div><div>https://github.com/bengeof/Compound2DeNovoDrugPropMax</div><div><br></div>Anticipating the rise in the use of quantum computing and quantum machine learning in drug discovery we use<br>the Penny-lane interface to quantum hardware to turn classical Keras layers used in our machine/deep<br>learning models into a quantum layer and introduce quantum layers into classical models to produce a<br>quantum-classical machine/deep learning hybrid model of our tool and the code corresponding to the<br><div>same is provided below</div><div><br></div>https://github.com/bengeof/QPoweredCompound2DeNovoDrugPropMax<br>

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