Efficient Gaussian sample specific network marker discovery and drug enrichment analysis validation

Background: Buyang Huanwu Tang (BYHWT) and relevant Traditional Chinese medicine (TCM) has its unique advantages in the treatment of cerebral ischemia. However, its pharmacological mechanism have not been fully explained. Objective: Base on the multi-component, also the entire disease network targets, the present study set out to identify major bioactive ingredients, key disease targets, and pathways of BYHWT against cerebral ischemia disease by systematic pharmacological methodology. Methods: Both the bioactive compounds from the BYHWT and the positive drugs against cerebral ischemia were fully investigated. The binding targets of the positive drugs were then obtained. A virtual screening protocol was then used to highlight the compound-target interaction. And network was constructed to visual the compound-target binding effect after docking analysis. Moreover，the targets enrichment analysis for biological processes and pathways were revealed to further explore the function of bio-targets protein gene and its role in the signal pathway. Results: A total of 382 active ingredients of the BYHWT and 23 candidate disease targets were identified. Virtual screening results indicated that multiple bioactive compounds targeted multiple proteins. Each compounds act on one or more targets. The mechanisms were linked to 20 signaling pathways, and the key mechanism was related to serotonergic synapse, calcium signaling pathway and camp signaling pathways. Conclusion: The present study explored the bioactive ingredients and mechanisms of BYHWT against cerebral ischemia by systematic pharmacological methodology. the novel methodology would provide a reference for the lead discovery of precursors, disease mechanism and material base for TCM.

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Identification of Candidate Genetic Markers and a Novel 4-genes Diagnostic Model in Osteoarthritis through Integrating Multiple Microarray Data

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323666200428120310 ◽

2020 ◽

Vol 23 (8) ◽

pp. 805-813

Author(s):

Ai Jiang ◽

Peng Xu ◽

Zhenda Zhao ◽

Qizhao Tan ◽

Shang Sun ◽

...

Keyword(s):

Signaling Pathway ◽

Microarray Data ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Mapk Signaling ◽

Functional Enrichment ◽

Joint Disease ◽

Support Vector ◽

Diagnostic Model ◽

Data Sets

Background: Osteoarthritis (OA) is a joint disease that leads to a high disability rate and a low quality of life. With the development of modern molecular biology techniques, some key genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA are still unknown. Objective: To develop a gene signature in OA. Method: In this study, five microarray data sets were integrated to conduct a comprehensive network and pathway analysis of the biological functions of OA related genes, which can provide valuable information and further explore the etiology and pathogenesis of OA. Results and Discussion: Differential expression analysis identified 180 genes with significantly expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological processes of negative regulation of kinase activity and some signaling pathways such as MAPK signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA specific protein-protein interaction (PPI) network was constructed based on the differentially expressed genes. The analysis of network topological attributes showed that differentially upregulated VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may influence the occurrence and development of OA through regulating cell cycle or apoptosis, and were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to establish the diagnosis model of OA, which not only had excellent predictive power in internal and external data sets (AUC > 0.9), but also had high predictive performance in different chip platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8). Conclusion: The 4-genes diagnostic model may be of great help to the early diagnosis and prediction of OA.

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Systematic Investigation of Quercetin for Treating Cardiovascular Disease Based on Network Pharmacology

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666190717124507 ◽

2019 ◽

Vol 22 (6) ◽

pp. 411-420 ◽

Cited By ~ 5

Author(s):

Xian-Jun Wu ◽

Xin-Bin Zhou ◽

Chen Chen ◽

Wei Mao

Keyword(s):

Cardiovascular Disease ◽

Cardiovascular Diseases ◽

Signaling Pathway ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Therapeutic Effects ◽

Pathway Enrichment ◽

Compound Target

Aim and Objective: Cardiovascular disease is a serious threat to human health because of its high mortality and morbidity rates. At present, there is no effective treatment. In Southeast Asia, traditional Chinese medicine is widely used in the treatment of cardiovascular diseases. Quercetin is a flavonoid extract of Ginkgo biloba leaves. Basic experiments and clinical studies have shown that quercetin has a significant effect on the treatment of cardiovascular diseases. However, its precise mechanism is still unclear. Therefore, it is necessary to exploit the network pharmacological potential effects of quercetin on cardiovascular disease. Materials and Methods: In the present study, a novel network pharmacology strategy based on pharmacokinetic filtering, target fishing, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, compound-target-pathway network structured was performed to explore the anti- cardiovascular disease mechanism of quercetin. Results:: The outcomes showed that quercetin possesses favorable pharmacokinetic profiles, which have interactions with 47 cardiovascular disease-related targets and 12 KEGG signaling pathways to provide potential synergistic therapeutic effects. Following the construction of Compound-Target-Pathway (C-T-P) network, and the network topological feature calculation, we obtained top 10 core genes in this network which were AKT1, IL1B, TNF, IL6, JUN, CCL2, FOS, VEGFA, CXCL8, and ICAM1. KEGG pathway enrichment analysis. These indicated that quercetin produced the therapeutic effects against cardiovascular disease by systemically and holistically regulating many signaling pathways, including Fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, MAPK signaling pathway, IL-17 signaling pathway and PI3K-Akt signaling pathway.

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Identification of Chronic Hypersensitivity Pneumonitis Biomarkers with Machine Learning and Differential Co-expression Analysis

Current Gene Therapy ◽

10.2174/1566523220666201208093325 ◽

2020 ◽

Vol 20 ◽

Author(s):

Hongwei Zhang ◽

Steven Wang ◽

Tao Huang

Keyword(s):

Feature Selection ◽

Expression Analysis ◽

Hypersensitivity Pneumonitis ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Great Promise ◽

Support Vector ◽

Svm Classifier ◽

Clinical Tool ◽

Chronic Hypersensitivity Pneumonitis

Aims: We would like to identify the biomarkers for chronic hypersensitivity pneumonitis (CHP) and facilitate the precise gene therapy of CHP. Background: Chronic hypersensitivity pneumonitis (CHP) is an interstitial lung disease caused by hypersensitive reactions to inhaled antigens. Clinically, the tasks of differentiating between CHP and other interstitial lungs diseases, especially idiopathic pulmonary fibrosis (IPF), were challenging. Objective: In this study, we analyzed the public available gene expression profile of 82 CHP patients, 103 IPF patients, and 103 control samples to identify the CHP biomarkers. Method: The CHP biomarkers were selected with advanced feature selection methods: Monte Carlo Feature Selection (MCFS) and Incremental Feature Selection (IFS). A Support Vector Machine (SVM) classifier was built. Then, we analyzed these CHP biomarkers through functional enrichment analysis and differential co-expression analysis. Result: There were 674 identified CHP biomarkers. The co-expression network of these biomarkers in CHP included more negative regulations and the network structure of CHP was quite different from the network of IPF and control. Conclusion: The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF. Many of the biomarker genes on the differential co-expression network showed great promise in revealing the underlying mechanisms of CHP.

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In Silico Protein Interaction Network Analysis of Virulence Proteins Associated with Invasive Aspergillosis for Drug Discovery

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666181120150633 ◽

2019 ◽

Vol 19 (2) ◽

pp. 146-155 ◽

Cited By ~ 1

Author(s):

Renu Chaudhary ◽

Meenakshi Balhara ◽

Deepak Kumar Jangir ◽

Mehak Dangi ◽

Mrridula Dangi ◽

...

Keyword(s):

Network Analysis ◽

Invasive Aspergillosis ◽

Protein Interaction ◽

Drug Target ◽

Interaction Network ◽

Enrichment Analysis ◽

Homology Model ◽

Ppi Network ◽

Virulence Proteins ◽

Hub Proteins

<P>Background: Protein-Protein interaction (PPI) network analysis of virulence proteins of Aspergillus fumigatus is a prevailing strategy to understand the mechanism behind the virulence of A. fumigatus. The identification of major hub proteins and targeting the hub protein as a new antifungal drug target will help in treating the invasive aspergillosis. </P><P> Materials & Method: In the present study, the PPI network of 96 virulence (drug target) proteins of A. fumigatus were investigated which resulted in 103 nodes and 430 edges. Topological enrichment analysis of the PPI network was also carried out by using STRING database and Network analyzer a cytoscape plugin app. The key enriched KEGG pathway and protein domains were analyzed by STRING.Conclusion:Manual curation of PPI data identified three proteins (PyrABCN-43, AroM-34, and Glt1- 34) of A. fumigatus possessing the highest interacting partners. Top 10% hub proteins were also identified from the network using cytohubba on the basis of seven algorithms, i.e. betweenness, radiality, closeness, degree, bottleneck, MCC and EPC. Homology model and the active pocket of top three hub proteins were also predicted.</P>

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