scholarly journals Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients

2021 ◽  
Author(s):  
Hong Wang ◽  
Jingqing Zhang ◽  
Zhigang Lu ◽  
Weina Dai ◽  
Chuanjiang Ma ◽  
...  
Keyword(s):  
Drug Targets ◽  
Drug Repurposing ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Protein Protein Interaction ◽  
Disease Module ◽  
Approved Drugs ◽  
Data Driven Modeling ◽  
The Relationship

Abstract After experiencing the COVID-19 pandemic, it is widely acknowledged that a rapid drug repurposing method is highly needed. A series of useful drug repurposing tools have been developed based on data-driven modeling and network pharmacology. Based on the disease module, we identified several hub proteins that play important roles in the onset and development of the COVID-19, which are potential targets for repositioning approved drugs. Moreover, different network distance metrics were applied to quantify the relationship between drug targets and COVID-19 disease targets in the protein–protein-interaction (PPI) network and predict COVID-19 therapeutic effects of bioactive herbal ingredients and chemicals. Furthermore, the tentative mechanisms of candidates were illustrated through molecular docking and gene enrichment analysis. We obtained 15 chemical and 15 herbal ingredient candidates and found that different drugs may play different roles in the process of virus invasion and the onset and development of the COVID-19 disease. Given pandemic outbreaks, our method has an undeniable immense advantage in the feasibility analysis of drug repurposing or drug screening, especially in the analysis of herbal ingredients.

scholarly journals A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing

2020 ◽  
Author(s):  
David E. Gordon ◽  
Gwendolyn M. Jang ◽  
Mehdi Bouhaddou ◽  
Jiewei Xu ◽  
Kirsten Obernier ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Affinity Purification ◽  
Drug Repurposing ◽  
Human Protein ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Interaction Map ◽  
Novel Coronavirus ◽  
Approved Drugs

ABSTRACTAn outbreak of the novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 290,000 people since the end of 2019, killed over 12,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven efficacy nor are there vaccines for its prevention. Unfortunately, the scientific community has little knowledge of the molecular details of SARS-CoV-2 infection. To illuminate this, we cloned, tagged and expressed 26 of the 29 viral proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), which identified 332 high confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 existing FDA-approved drugs, drugs in clinical trials and/or preclinical compounds, that we are currently evaluating for efficacy in live SARS-CoV-2 infection assays. The identification of host dependency factors mediating virus infection may provide key insights into effective molecular targets for developing broadly acting antiviral therapeutics against SARS-CoV-2 and other deadly coronavirus strains.

scholarly journals In silico synergistic drug repurposing for combating novel coronavirus (COVID-19) outbreaks

2020 ◽  
Author(s):  
Minjee Kim ◽  
Young Bong Kim
Keyword(s):  
Drug Development ◽  
Drug Combination ◽  
Drug Repurposing ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Target List ◽  
Drug Candidates ◽  
Novel Coronavirus ◽  
Approved Drugs

Abstract As the number of novel coronavirus (COVID-19) cases continues to rise, there is a global need for rapid drug development. In this study, we propose a systems pharmacology approach to reposition FDA-approved drug candidates for coronavirus, identify targets and suggest a synergistic drug combination using network pharmacology. We collected 67 genes associated with coronavirus, performed an enrichment analysis to obtain coronavirus-associated disease- pathway and constructed protein-protein interaction (PPI) network based on 67 genes. Total 37 significant disease-pathways were retrieved, and associated FDA-approved drugs were listed for drug repurposing candidates. Our PPI network showed 51 targets from 67 genes and identified IL6 and TNF as potential targets for coronavirus. From the FDA drug list, we selected four drugs that are experimentally used or studied for coronavirus to construct two- drug combinations. From six drug-drug networks, we identified hydroxychloroquine + ribavirin combination had the highest number of overlapping targets (IL6, IL2, IL10, CASP3, IFNA1) from PPI network target list, suggesting a potent synergistic drug combination for coronavirus. With the aim to support the rapid drug development, we suggest a new approach using systems-level drug repurposing for COVID-19 treatment.

scholarly journals Network Pharmacology Dissection of Multi-scale Mechanisms of Action of Tu-Xian Mixture for the Treatment of Diabetic Cognitive Impairment

2020 ◽  
Author(s):  
Bin Yan ◽  
Qing Sun ◽  
Jianhong Sun ◽  
Guoqing Tian
Keyword(s):  
Cognitive Impairment ◽  
Pathway Analysis ◽  
Drug Targets ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Kegg Pathway Analysis ◽  
Go Enrichment

Abstract Background Traditional Chinese medicine (TCM) has long been used in the treatment of diabetic cognitive impairment (DCI), and Tu-Xian mixture is one of the prescriptions. This study used a network pharmacology method to predict the active ingredients, potential targets and pathways of Tu-Xian mixture in the treatment of DCI.Methods The potential active ingredients of Tu-xian mixture were obtained by querying databases such as TCMSP, drug targets were screened through Pharmmapper, ETCM, and UniProt databases, and disease target protein groups were collected using TTD, OMIM, and CTD databases. Therapeutic targets were obtained by comparative analysis. Then use WebGestalt to complete GO enrichment analysis and KEGG pathway analysis.Results A total of 51 active ingredients and 619 potential targets were obtained, among which there were 33 key targets for diabetes and 18 key targets for cognitive impairment. After GO analysis and KEGG pathway analysis, steroid hormone biosynthesis and γ-aminobutyric acid signaling pathway may be the important metabolic pathways for Tu-Xian mixture to perform therapeutic effects.Conclusions This work promoted the comprehension of Tu-Xian mixture in the treatment of DCI, and further experimental verification of the prediction results was still needed to support its clinical application.

scholarly journals A Network-Based Approach to Explore the Mechanisms of Uncaria Alkaloids in Treating Hypertension and Alleviating Alzheimer’s Disease

2020 ◽  
Vol 21 (5) ◽  
pp. 1766 ◽  
Author(s):  
Wenyong Wu ◽  
Zijia Zhang ◽  
Feifei Li ◽  
Yanping Deng ◽  
Min Lei ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Targets ◽  
Biological Activities ◽  
Target Prediction ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Protein Protein Interaction ◽  
Novel Strategy ◽  
Underlying Mechanisms

Uncaria alkaloids are the major bioactive chemicals found in the Uncaria genus, which have a long history of clinical application in treating cardiovascular and mental diseases in traditional Chinese medicine (TCM). However, there are gaps in understanding the multiple targets, pathways, and biological activities of Uncaria alkaloids. By constructing the interactions among drug-targets-diseases, network pharmacology provides a systemic methodology and a novel perspective to present the intricate connections among drugs, potential targets, and related pathways. It is a valuable tool for studying TCM drugs with multiple indications, and how these multi-indication drugs are affected by complex interactions in the biological system. To better understand the mechanisms and targets of Uncaria alkaloids, we built an integrated analytical platform based on network pharmacology, including target prediction, protein–protein interaction (PPI) network, topology analysis, gene enrichment analysis, and molecular docking. Using this platform, we revealed the underlying mechanisms of Uncaria alkaloids’ anti-hypertensive effects and explored the possible application of Uncaria alkaloids in preventing Alzheimer’s disease. These results were further evaluated and refined using biological experiments. Our study provides a novel strategy for understanding the holistic pharmacology of TCM, as well as for exploring the multi-indication properties of TCM beyond its traditional applications.

scholarly journals Network Pharmacology-based Prediction of Mechanism of Shenzhuo Formula for Application to DN

2020 ◽  
Author(s):  
Xin-miao Wang ◽  
Lin Han ◽  
Li-li Zhang ◽  
Sha Di ◽  
Xiu-xiu Wei ◽  
...  
Keyword(s):  
Rna Polymerase Ii ◽  
Signaling Pathway ◽  
Drug Targets ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Regulation Of Transcription ◽  
Therapeutic Effects ◽  
Go Analysis ◽  
Pathway Enrichment

Abstract Background: Shenzhuo formula is a traditional Chinese medicine (TCM) prescription which has significant therapeutic effects on diabetic nephropathy (DN). However, its mechanism remains unknown. Therefore, this study aimed to explore the underlying anti-DN mechanism of shenzhuo formula.Methods: The active ingredients and targets of shenzhuo formula were obtained by searching TCMSP, TCMID, SwissTargetPrediction and HIT. The DN target was identified from TTD, DrugBank and DisGeNet. The potential targets were obtained and PPI network were built after mapping the disease and drug targets. The key targets were screened out by network topology and the “drugs - DN - key targets” network was constructed by Cytoscape. GO analysis and KEGG pathway enrichment analysis were performed using DAVID, and the results were visualized using the Omicshare Tools.Results: We obtained 182 potential targets and 30 key targets. Ulteriorly, “drugs - DN - key targets” network were constructed, and results showed that nodes like M51, M21, M5, M71, M28, EGFR, MMP9, MAPK8, PIK3CA and STAT3 had a higher degree. GO analysis results mainly involved in positive regulation of transcription from RNA polymerase II promoter, inflammatory response, lipopolysaccharide-mediated signalingpathway and other biological processes. The results of KEGG showed that DN-related pathways like TNF signaling pathway, PI3K-Akt signaling pathway were at the top of the list.Conclusion: This article reveals the possible mechanism of shenzhuo formula in the treatment of DN through network pharmacology research, and lays a foundation for further studies.

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

2019 ◽  
Vol 22 (6) ◽  
pp. 411-420 ◽  
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.

scholarly journals Elucidating the Mechanisms of Action of Lianhua Qingwen decoction for the Treatment of COVID-19 via Systems Pharmacology Approaches

2021 ◽  
Author(s):  
Xiting Wang ◽  
Tao Lu
Keyword(s):  
Molecular Docking ◽  
Blood Circulation ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Detection Algorithm ◽  
Network Pharmacology ◽  
Systems Pharmacology ◽  
Protein Protein Interaction ◽  
Further Development ◽  
Protein Protein Interaction Network

Abstract Due to the severity of the COVID-19 epidemic, to identify a proper treatment for COVID-19 is of great significance. Traditional Chinese Medicine (TCM) has shown its great potential in the prevention and treatment of COVID-19. One of TCM decoction, Lianhua Qingwen decoction displayed promising treating efficacy. Nevertheless, the underlying molecular mechanism has not been explored for further development and treatment. Through systems pharmacology and network pharmacology approaches, we explored the potential mechanisms of Lianhua Qingwen treating COVID-19 and acting ingredients of Lianhua Qingwen decoction for COVID-19 treatment. Through this way, we generated an ingredients-targets database. We also used molecular docking to screen possible active ingredients. Also, we applied the protein-protein interaction network and detection algorithm to identify relevant protein groupings of Lianhua Qingwen. Totally, 605 ingredients and 1,089 targets were obtained. Molecular Docking analyses revealed that 35 components may be the promising acting ingredients, 7 of which were underlined according to the comprehensive analysis. Our enrichment analysis of the 7 highlighted ingredients showed relevant significant pathways that could be highly related to their potential mechanisms, e.g. oxidative stress response, inflammation, and blood circulation. In summary, this study suggests the promising mechanism of the Lianhua Qingwen decoction for COVID-19 treatment. Further experimental and clinical verifications are still needed.

scholarly journals Analysis of the Molecular Mechanisms of the Effects of Prunella vulgaris against Subacute Thyroiditis Based on Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xin Shen ◽  
Rui Yang ◽  
Jianpeng An ◽  
Xia Zhong
Keyword(s):  
Signaling Pathways ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Subacute Thyroiditis ◽  
Network Pharmacology ◽  
Pharmacokinetic Parameters ◽  
Pathway Enrichment Analysis ◽  
Prunella Vulgaris ◽  
Protein Protein Interaction

Prunella vulgaris (PV) has a long history of application in traditional Chinese and Western medicine as a remedy for the treatment of subacute thyroiditis (SAT). This study applied network pharmacology to elucidate the mechanism of the effects of PV against SAT. Components of the potential therapeutic targets of PV and SAT-related targets were retrieved from databases. To construct a protein-protein interaction (PPI) network, the intersection of SAT-related targets and PV-related targets was input into the STRING platform. Gene ontology (GO) analysis and KEGG pathway enrichment analysis were carried out using the DAVID database. Networks were constructed by Cytoscape for visualization. The results showed that a total of 11 compounds were identified according to the pharmacokinetic parameters of ADME. A total of 126 PV-related targets and 2207 SAT-related targets were collected, and 83 overlapping targets were subsequently obtained. The results of the KEGG pathway and compound-target-pathway (C-T-P) network analysis suggested that the anti-SAT effect of PV mainly occurs through quercetin, luteolin, kaempferol, and beta-sitosterol and is most closely associated with their regulation of inflammation and apoptosis by targeting the PIK3CG, MAPK1, MAPK14, TNF, and PTGS2 proteins and the PI3K-Akt and TNF signaling pathways. The study demonstrated that quercetin, luteolin, kaempferol, and beta-sitosterol in PV may play a major role in the treatment of SAT, which was associated with the regulation of inflammation and apoptosis, by targeting the PI3K-Akt and TNF signaling pathways.

scholarly journals A Network Pharmacology Approach to Uncover the Mechanisms of Shen-Qi-Di-Huang Decoction against Diabetic Nephropathy

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Sha Di ◽  
Lin Han ◽  
Qing Wang ◽  
Xinkui Liu ◽  
Yingying Yang ◽  
...  
Keyword(s):  
Diabetic Nephropathy ◽  
Signaling Pathway ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Regulation Of Blood Pressure ◽  
Protein Protein Interaction Network

Shen-Qi-Di-Huang decoction (SQDHD), a well-known herbal formula from China, has been widely used in the treatment of diabetic nephropathy (DN). However, the pharmacological mechanisms of SQDHD have not been entirely elucidated. At first, we conducted a comprehensive literature search to identify the active constituents of SQDHD, determined their corresponding targets, and obtained known DN targets from several databases. A protein-protein interaction network was then built to explore the complex relations between SQDHD targets and those known to treat DN. Following the topological feature screening of each node in the network, 400 major targets of SQDHD were obtained. The pathway enrichment analysis results acquired from DAVID showed that the significant bioprocesses and pathways include oxidative stress, response to glucose, regulation of blood pressure, regulation of cell proliferation, cytokine-mediated signaling pathway, and the apoptotic signaling pathway. More interestingly, five key targets of SQDHD, named AKT1, AR, CTNNB1, EGFR, and ESR1, were significant in the regulation of the above bioprocesses and pathways. This study partially verified and predicted the pharmacological and molecular mechanisms of SQDHD on DN from a holistic perspective. This has laid the foundation for further experimental research and has expanded the rational application of SQDHD in clinical practice.

scholarly journals CSNK1A1 and Gli2 as Novel Targets Identified through an Integrative Analysis of Gene Expression Data, Protein–Protein Interaction and Pathways Networks in Glioblastoma Tumors: Can these Two be Antagonistic Proteins?

2014 ◽  
Vol 13 ◽  
pp. CIN.S18377 ◽  
Author(s):  
Seema Mishra
Keyword(s):  
Signaling Pathways ◽  
Drug Targets ◽  
Active Role ◽  
Integrative Approach ◽  
Significant Differential Expression ◽  
Shh Signaling ◽  
Protein Protein Interaction ◽  
Shh Pathway ◽  
Expression Of Genes ◽  
The Relationship

Glioblastoma (GBM) is the malignant form of glioma, and the interplay of different pathways working in concert in GBM development and progression needs to be fully understood. Wnt signaling and sonic hedgehog (SHH) signaling pathways, having basic similarities, are among the major pathways aberrantly activated in GBM, and hence, need to be targeted. It becomes imperative, therefore, to explore the functioning of these pathways in context of each other in GBM. An integrative approach may help provide new biological insights, as well as solve the problem of identifying common drug targets for simultaneous targeting of these pathways. The beauty of this approach is that it can recapitulate several known facts, as well as decipher new emerging patterns, identifying those targets that could be missed when relying on one type of data at a time. This approach can be easily extended to other systems to discover key patterns in the functioning of signaling molecules. Studies were designed to assess the relationship between significant differential expression of genes of the Wnt (Wnt/β-catenin canonical and Wnt non-canonical) and SHH signaling pathways and their connectivity patterns in interaction and signaling networks. Further, the aim was to decipher underlying mechanistic patterns that may be involved in a more specific way and to generate a ranked list of genes that can be used as markers or drug targets. These studies predict that Wnt pathway plays a relatively more pro-active role than the SHH pathway in GBM. Further, CTNNB1, CSNK1A1, and Gli2 proteins may act as key drug targets common to these pathways. While CTNNB1 is a widely studied molecule in the context of GBM, the likely roles of CSNK1A1 and Gli2 are found to be relatively novel. It is surmised that Gli2 may be antagonistic to CSNK1A1, preventing the phosphorylation of CTNNB1 and SMO proteins in Wnt and SHH signaling pathway, respectively, by CSNK1A1, and thereby, aberrant activation. New insights into the possible behavior of these pathway molecules relative to each other in GBM reveal some key interesting patterns.

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