Glycyrrhizic Acid for COVID-19: Findings of Targeting Pivotal Inflammatory Pathways Triggered by SARS-CoV-2

Background: Coronavirus disease 2019 (COVID-19) is now a worldwide public health crisis. The causative pathogen is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Novel therapeutic agents are desperately needed. Because of the frequent mutations in the virus and its ability to cause cytokine storms, targeting the viral proteins has some drawbacks. Targeting cellular factors or pivotal inflammatory pathways triggered by SARS-CoV-2 may produce a broader range of therapies. Glycyrrhizic acid (GA) might be beneficial against SARS-CoV-2 because of its anti-inflammatory and antiviral characteristics and possible ability to regulate crucial host factors. However, the mechanism underlying how GA regulates host factors remains to be determined.Methods: In our report, we conducted a bioinformatics analysis to identify possible GA targets, biological functions, protein-protein interactions, transcription-factor-gene interactions, transcription-factor-miRNA coregulatory networks, and the signaling pathways of GA against COVID-19.Results: Protein-protein interactions and network analysis showed that ICAM1, MMP9, TLR2, and SOCS3 had higher degree values, which may be key targets of GA for COVID-19. GO analysis indicated that the response to reactive oxygen species was significantly enriched. Pathway enrichment analysis showed that the IL-17, IL-6, TNF-α, IFN signals, complement system, and growth factor receptor signaling are the main pathways. The interactions of TF genes and miRNA with common targets and the activity of TFs were also recognized.Conclusions: GA may inhibit COVID-19 through its anti-oxidant, anti-viral, and anti-inflammatory effects, and its ability to activate the immune system, and targeted therapy for those pathways is a predominant strategy to inhibit the cytokine storms triggered by SARS-CoV-2 infection.

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pathDIP 4: an extended pathway annotations and enrichment analysis resource for human, model organisms and domesticated species

Nucleic Acids Research ◽

10.1093/nar/gkz989 ◽

2019 ◽

Cited By ~ 5

Author(s):

Sara Rahmati ◽

Mark Abovsky ◽

Chiara Pastrello ◽

Max Kotlyar ◽

Richard Lu ◽

...

Keyword(s):

Protein Interactions ◽

Enrichment Analysis ◽

Human Model ◽

Model Organisms ◽

Pathway Enrichment Analysis ◽

Protein Protein Interactions ◽

Proteome Coverage ◽

Pathway Annotation ◽

Human Proteins ◽

Integrated Pathway

Abstract PathDIP was introduced to increase proteome coverage of literature-curated human pathway databases. PathDIP 4 now integrates 24 major databases. To further reduce the number of proteins with no curated pathway annotation, pathDIP integrates pathways with physical protein–protein interactions (PPIs) to predict significant physical associations between proteins and curated pathways. For human, it provides pathway annotations for 5366 pathway orphans. Integrated pathway annotation now includes six model organisms and ten domesticated animals. A total of 6401 core and ortholog pathways have been curated from the literature or by annotating orthologs of human proteins in the literature-curated pathways. Extended pathways are the result of combining these pathways with protein-pathway associations that are predicted using organism-specific PPIs. Extended pathways expand proteome coverage from 81 088 to 120 621 proteins, making pathDIP 4 the largest publicly available pathway database for these organisms and providing a necessary platform for comprehensive pathway-enrichment analysis. PathDIP 4 users can customize their search and analysis by selecting organism, identifier and subset of pathways. Enrichment results and detailed annotations for input list can be obtained in different formats and views. To support automated bioinformatics workflows, Java, R and Python APIs are available for batch pathway annotation and enrichment analysis. PathDIP 4 is publicly available at http://ophid.utoronto.ca/pathDIP.

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Identification of Potential Core Genes and Potential Drugs in Vestibular Schwannoma

10.21203/rs.3.rs-715254/v1 ◽

2021 ◽

Author(s):

Junqiang Yan ◽

Anran Liu ◽

Jiarui Huang ◽

Jiannan Wu ◽

Hongxia Ma ◽

...

Keyword(s):

Vestibular Schwannoma ◽

Protein Interactions ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Pathway Enrichment Analysis ◽

Data Sets ◽

Protein Protein Interactions ◽

Potential Core ◽

Normal Tissues ◽

Therapeutic Drugs

Abstract Vestibular schwannoma is a common intracranial benign tumor, but the current drug treatment effect is not obvious. Surgical treatment can usually lead to residual problems such as nerve damage. Therefore, there is no clear molecular target to facilitate better clinical treatment. We analyzed three microarray data sets (GSE39645, GSE54934 and GSE108524) derived from the Gene Expression Omnibus database (GEO). The GEO2R was used to screen for the differentially expressed genes (DEG) between vestibular schwannomas and normal tissues. The ontology function of genes and genome pathway enrichment analysis were performed using annotation, visualizative and comprehensive discovery databases to identify the pathways and functional annotation of DEGs. The protein-protein interactions of these DEGs were analyzed by searching the interaction gene database and visualized by Cytoscape software. The potential therapeutic drugs for vestibular schwannoma were searched by online gene drug interaction analysis.A total of 226 up-regulated and 148 down-regulated DEGs were identified. Among them, ten hub genes with high connectivity (EGFR, PPARG, CD86, CSF1R, SPP1, CDH2, CCND1, CAV1, CYBB and NCAM1) were selected as the central genes that may be closely related to the pathogenesis of vestibular schwannoma, which can be potential treatment targets of vestibular schwannoma. Afatinib and osimerinib may be potential therapeutic drugs.

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Predicting the Molecular Mechanism of Sini Jia Renshen Decoction in Treating Severe COVID-19 Patients Based on Network Pharmacology and Molecular Docking

Natural Product Communications ◽

10.1177/1934578x211059292 ◽

2021 ◽

Vol 16 (12) ◽

pp. 1934578X2110592

Author(s):

Yi Wen Liu ◽

Ai Xia Yang ◽

Li Lu ◽

Tie Hua Huang

Keyword(s):

Molecular Docking ◽

Protein Interactions ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Protein Protein Interactions ◽

Active Ingredients ◽

Active Compounds ◽

Pathway Network ◽

Function Enrichment Analysis

Objective: To explore the potential mechanism of Sini jia Renshen Decoction (SJRD) in the treatment of COVID-19 based on network pharmacology and molecular docking. Methods: The active compounds and potential therapeutic targets of SJRD were collected through the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). Then a string database was used to build a protein–protein interactions (PPI) network between proteins, and use the David database to perform gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core targets. Then we used Cytoscape software to construct an active ingredients-core target-signaling pathway network, and finally the active ingredients of SJRD were molecularly docked with the core targets to predict the mechanism of SJRD in the treatment of COVID-19. Results: A total of 136 active compounds, 51 core targets and 93 signaling pathways were selected. Molecular docking results revealed that quercetin, 3,22-dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid, 18α-hydroxyglycyrrhetic acid, gomisin B and ignavine had considerable binding ability with ADRB2, PRKACA, DPP4, PIK3CG and IL6. Conclusions: This study preliminarily explored the mechanism of multiple components，multiple targets，and multiple pathways of SJRD in the treatment of COVID-19 by network pharmacology．

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Predicting the Molecular Mechanism of Shenling Baizhu San in Treating Convalescent Patients With COVID-19 Based on Network Pharmacology and Molecular Docking

Natural Product Communications ◽

10.1177/1934578x211046069 ◽

2021 ◽

Vol 16 (10) ◽

pp. 1934578X2110460

Author(s):

Ying Zhang ◽

Li Lu ◽

YiWen Liu ◽

AiXia Yang ◽

Yanfang Yang

Keyword(s):

Molecular Docking ◽

Protein Interactions ◽

Enrichment Analysis ◽

Binding Energies ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Protein Protein Interactions ◽

Active Ingredients ◽

Active Components ◽

Pathway Network

Objective: Shenling Baizhu San (SBS) was selected as the regimen for the treatment of COVID-19 in Guangdong Province. It is mainly used for the convalescent treatment of COVID-19 patients with deficiency of both lung and spleen. In this study, we aimed to explore the mechanism of SBS in the treatment of COVID-19 through network pharmacology combined with molecular docking. Methods: The targets of active components of SBS were collected through Traditional Chinese Medicine Systems Pharmacology (TCMSP) and ETCM databases. Using the Genecards, TTD, OMIM and other databases, the targets of COVID-19 were determined. The next step was to use a string database to build a protein–protein interactions (PPI) network between proteins, and use David database to perform gene ontology (GO) function enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core targets. Then we used Cytoscape software to construct the active ingredients-core target-signaling pathway network, and finally the active ingredients of SBS were molecularly docked with the core targets to predict the mechanism of SBS in the treatment of COVID-19. Results: A total of 177 active compounds, 43 core targets and 58 signaling pathways were selected. Molecular docking results showed that the binding energies of the top six active components and the targets were all less than −5 kcal/MOL. Conclusion: The potential mechanism of action of SBS in the treatment of COVID-19 may be associated with the regulation of genes co-expressed with IL6, DPP4, PTGS2, PTGS1 and TNF.

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A computational interactome and functional annotation for the human proteome

eLife ◽

10.7554/elife.18715 ◽

2016 ◽

Vol 5 ◽

Cited By ~ 32

Author(s):

José Ignacio Garzón ◽

Lei Deng ◽

Diana Murray ◽

Sagi Shapira ◽

Donald Petrey ◽

...

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Enrichment Analysis ◽

Human Proteome ◽

Gene Set Enrichment Analysis ◽

Protein Protein Interactions ◽

Functional Relationships ◽

Structural Relationships ◽

Human Proteins ◽

Validation Tests

We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein’s function. We provide annotations for most human proteins, including many annotated as having unknown function.

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Network Pharmacology-Based Strategy to Investigate Pharmacological Mechanisms of the Drug Pair Astragalus-Angelica for Treatment of Male Infertility

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/8281506 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Feng Zhao ◽

Yingjun Deng ◽

Guanchao Du ◽

Shengjing Liu ◽

Jun Guo ◽

...

Keyword(s):

Male Infertility ◽

Signaling Pathway ◽

Mechanism Of Action ◽

Protein Interactions ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Protein Protein Interactions ◽

Drug Pair ◽

Effective Components ◽

New Ideas

Background. The traditional Chinese medicines Astragalus and Angelica are often combined to treat male infertility, but the specific therapeutic mechanism is not clear. Therefore, this study applies a network pharmacology approach to investigate the possible mechanism of action of the drug pair Astragalus-Angelica (PAA) in the treatment of male infertility. Methods. Relevant targets for PAA treatment of male infertility are obtained through databases. Protein-protein interactions (PPIs) are constructed through STRING database and screen core targets, and an enrichment analysis is conducted through the Metascape platform. Finally, molecular docking experiments were carried out to evaluate the affinity between the target protein and the ligand of PAA. Results. The active ingredients of 112 PAA, 980 corresponding targets, and 374 effective targets of PAA for the treatment of male infertility were obtained, which are related to PI3K-Akt signaling pathway, HIF-1 signaling pathway, AGE-RAGE signaling pathway, IL-17 signaling pathway, and thyroid hormone signaling pathway. Conclusion. In this study, using a network pharmacology method, we preliminarily analyzed the effective components and action targets of the PAA. We also explored the possible mechanism of action of PAA in treating male infertility. They also lay a foundation for expanding the clinical application of PAA and provide new ideas and directions for further research on the mechanisms of action of the PAA and its components for male infertility treatment.

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Systems Pharmacological Approach to Investigate the Mechanism of Acori Tatarinowii Rhizoma for Alzheimer’s Disease

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2018/5194016 ◽

2018 ◽

Vol 2018 ◽

pp. 1-20 ◽

Cited By ~ 5

Author(s):

Zhenyan Song ◽

Fang Yin ◽

Biao Xiang ◽

Bin Lan ◽

Shaowu Cheng

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Therapeutic Effects ◽

Biological Processes ◽

Pathway Enrichment ◽

Study Results ◽

Target Network ◽

Anti Inflammatory

In traditional Chinese medicine (TCM), Acori Tatarinowii Rhizoma (ATR) is widely used to treat memory and cognition dysfunction. This study aimed to confirm evidence regarding the potential therapeutic effect of ATR on Alzheimer’s disease (AD) using a system network level based in silico approach. Study results showed that the compounds in ATR are highly connected to AD-related signaling pathways, biological processes, and organs. These findings were confirmed by compound-target network, target-organ location network, gene ontology analysis, and KEGG pathway enrichment analysis. Most compounds in ATR have been reported to have antifibrillar amyloid plaques, anti-tau phosphorylation, and anti-inflammatory effects. Our results indicated that compounds in ATR interact with multiple targets in a synergetic way. Furthermore, the mRNA expressions of genes targeted by ATR are elevated significantly in heart, brain, and liver. Our results suggest that the anti-inflammatory and immune system enhancing effects of ATR might contribute to its major therapeutic effects on Alzheimer’s disease.

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Quantitative proteomics analysis of FFPE tumor samples reveals the influences of novel targeting nanobubbles conjugated with NET-1 siRNA on tetraspanin protein involved in HCC

10.21203/rs.3.rs-20923/v1 ◽

2020 ◽

Author(s):

Bolin Wu ◽

Haitao Shang ◽

Xitian Liang ◽

Huajing Yang Huajing Yang ◽

Hui Jing ◽

...

Keyword(s):

Protein Interactions ◽

Interaction Analysis ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Differentially Expressed Proteins ◽

Label Free ◽

Protein Protein Interaction ◽

Study Results ◽

Ffpe Tumor

Abstract Background: Hepatocellular carcinoma (HCC) poses a severe threat to human health. The NET-1 protein has been proved to be strongly associated with HCC proliferation and metastasis in our previous study. Methods: Here, we developed a label-free proteome mass spectrometry workflow to analyze formalin-fixed and paraffin-embedded HCC xenograft samples collected in our previous study. Results: The result showed that 78 proteins were differentially expressed after NET-1 protein inhibited. Among them, the expression of 61 proteins up-regulated and the expression of 17 proteins were significantly down-regulated. Of the differentially expressed proteins, the vast majority of Gene Ontology enrichment terms belong to the biological process. The KEGG pathway enrichment analysis showed that the 78 differentially expressed proteins significantly enriched in 45 pathways. We concluded that the function of the NET-1 gene is not only to regulate HCC but also to participate in a variety of biochemical metabolic pathways in the human body. Furthermore, the protein-protein interaction analysis indicated that the interactions of differentially expressed proteins are incredibly sophisticated. All the protein-protein interactions happened after the NET-1 gene has been silenced. Conclusions: Finally, our study also provides a useful proposal for targeted therapy based on tetraspanin proteins to treat HCC, and further mechanism investigations are needed to reveal a more detailed mechanism of action for NET-1 protein regulation of HCC.

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Novel Comprehensive Bioinformatics Approaches to Determine the Molecular Genetic Susceptibility Profile of Moderate and Severe Asthma

International Journal of Molecular Sciences ◽

10.3390/ijms21114022 ◽

2020 ◽

Vol 21 (11) ◽

pp. 4022

Author(s):

Hatem Zayed

Keyword(s):

Severe Asthma ◽

Protein Interactions ◽

Gene Networks ◽

Molecular Genetic ◽

Enrichment Analysis ◽

Protein Protein Interactions ◽

Bioinformatics Pipeline ◽

Moderate Asthma ◽

Asthma Phenotypes ◽

Chronic Inflammatory Condition

Background: Asthma is a chronic inflammatory condition linked to hyperresponsiveness in the airways. There is currently no cure available for asthma, and therapy choices are limited. Asthma is the result of the interplay between genes and the environment. The exact molecular genetic mechanism of asthma remains elusive. Aims: The aim of this study is to provide a comprehensive, detailed molecular etiology profile for the molecular factors that regulate the severity of asthma and pathogenicity using integrative bioinformatics tools. Methods: The GSE43696 omnibus gene expression dataset, which contains 50 moderate cases, 38 severe cases, and 20 healthy controls, was used to investigate differentially expressed genes (DEGs), susceptible chromosomal loci, gene networks, pathways, gene ontologies, and protein–protein interactions (PPIs) using an intensive bioinformatics pipeline. Results: The PPI network analysis yielded DEGs that contribute to interactions that differ from moderate-to-severe asthma. The combined interaction scores resulted in higher interactions for the genes STAT3, AGO2, COL1A1, CLCN6, and KSR for moderate asthma and JAK2, INSR, ERBB2, NR3C1, and PTK6 for severe asthma. Enrichment analysis (EA) demonstrated differential enrichment between moderate and severe asthma phenotypes; the ion transport regulation pathway was significantly enhanced in severe asthma phenotypes compared to that in moderate asthma phenotypes and involved PER2, GCR, IRS-2, KCNK7, KCNK6, NOX1, and SCN7A. The most enriched common pathway in both moderate and severe asthma is the development of the glucocorticoid receptor (GR) signaling pathway followed by glucocorticoid-mediated inhibition of proinflammatory and proconstrictory signaling in the airway of smooth muscle cell pathways. Gene sets were shared between severe and moderate asthma at 16 chromosome locations, including 17p13.1, 16p11.2, 17q21.31, 1p36, and 19q13.2, while 60 and 48 chromosomal locations were unique for both moderate and severe asthma, respectively. Phylogenetic analysis for DEGs showed that several genes have been intersected in phases of asthma in the same cluster of genes. This could indicate that several asthma-associated genes have a common ancestor and could be linked to the same biological function or gene family, implying the importance of these genes in the pathogenesis of asthma. Conclusion: New genetic risk factors for the development of moderate-to-severe asthma were identified in this study, and these could provide a better understanding of the molecular pathology of asthma and might provide a platform for the treatment of asthma.

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