Network Pharmacology study on the mechanism of MKA Polyherbal Formulation in combating Respiratory Diseases

The Multi-targeted action of Polyherbal formulation is responsible for enhanced therapeutic efficacy in combating various diseases. But, understanding the mode of action of herbal medicine remains a challenge because of its complex metabolomics. Network pharmacology-based approach enables to explore the mechanism of action of polyherbal formulation in biological system. In present investigation, we have explored the molecular mechanism of action of the Polyherbal formulation MKA comprising of three botanicals Mimusops elengi L., Kedrostis foetidissima (Jacq.) Cogn. and Artemisia vulgaris L. in treating respiratory diseases by network pharmacology-based approach. The protein targets were mined from Binding database for the bioactive present in MKA. The disease associated targets were identified using Open target Platform. Based on ligand-target interactions, it was interpreted that MKA could alleviate the symptoms of respiratory disease by multiple mechanisms like EGFR inhibition by Quercetin and Quercetin-3-O-rhamnoside, KDR inhibition by Quercetin, STAT-3 inhibition by β-sitosterol- β-Dglucoside, TRPV1 inhibition by phytol acetate, etc. The Protein-protein interaction (PPI) network was constructed using STRING database. KEGG pathway based functional enrichment was also predicted for the PPI network. It was found that multiple ligand-target interactions and protein-protein interactions is responsible for pharmacological activity of MKA in respiratory diseases.

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Comparison of Computational Tools for Protein-Protein Interaction (PPI) Mapping and Analysis

Jurnal Teknologi ◽

10.11113/jt.v63.1226 ◽

2013 ◽

Vol 63 (1) ◽

Cited By ~ 1

Author(s):

Geok Wei Leong ◽

Sheau Chen Lee ◽

Cher Chien Lau ◽

Peter Klappa ◽

Mohd Shahir Shamsir Omar

Keyword(s):

Protein Interactions ◽

Network Visualization ◽

Database Integration ◽

File Format ◽

Output File ◽

Ppi Network ◽

Protein Protein Interactions ◽

Computational Tools ◽

Protein Protein Interaction ◽

Visualization Tools

Several visualization tools for the mapping of protein-protein interactions have been developed in recent years. However, a systematic comparison of the virtues and limitations of different PPI visualization tools has not been carried out so far. In this study, we compare seven commonly used visualization tools, based on input and output file format, layout algorithm, database integration, Gene Ontology annotation and accessibility of each tool. The assessment was carried out based on brain disease datasets. Our suggested tools, NAViGaTOR, Cytoscape and Gephi perform competitively as PPI network visualization tools, can be a reference for future researches on PPI mapping and analysis.

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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 Study on the Antirheumatic Mechanism of Tibetan Medicated-Bath Therapy Using Wuwei-Ganlu-Yaoyu-Keli

BioMed Research International ◽

10.1155/2017/2320932 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10

Author(s):

Tianhong Wang ◽

Jian Yang ◽

Xing Chen ◽

Kehui Zhao ◽

Jing Wang ◽

...

Keyword(s):

Mechanism Of Action ◽

Protein Interactions ◽

Animal Experiment ◽

Tibetan Medicine ◽

Network Pharmacology ◽

Expression Data ◽

Protein Protein Interactions ◽

Microarray Experiments ◽

Traditional Tibetan Medicine ◽

Bath Therapy

In clinical practice at Tibetan area of China, Traditional Tibetan Medicine formula Wuwei-Ganlu-Yaoyu-Keli (WGYK) is commonly added in warm water of bath therapy to treat rheumatoid arthritis (RA). However, its mechanism of action is not well interpreted yet. In this paper, we first verify WGYK’s anti-RA effect by an animal experiment. Then, based on gene expression data from microarray experiments, we apply approaches of network pharmacology to further reveal the mechanism of action for WGYK to treat RA by analyzing protein-protein interactions and pathways. This study may facilitate our understanding of anti-RA effect of WGYK from perspective of network pharmacology.

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Identifying Hierarchical and Overlapping Protein Complexes Based on Essential Protein-Protein Interactions and “Seed-Expanding” Method

BioMed Research International ◽

10.1155/2014/838714 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12

Author(s):

Jun Ren ◽

Wei Zhou ◽

Jianxin Wang

Keyword(s):

Protein Interactions ◽

Time Complexity ◽

Protein Complexes ◽

Hierarchical Organization ◽

Experimental Results ◽

Functional Enrichment ◽

Detection Methods ◽

Ppi Network ◽

Protein Protein Interactions ◽

Ppi Networks

Many evidences have demonstrated that protein complexes are overlapping and hierarchically organized in PPI networks. Meanwhile, the large size of PPI network wants complex detection methods have low time complexity. Up to now, few methods can identify overlapping and hierarchical protein complexes in a PPI network quickly. In this paper, a novel method, called MCSE, is proposed based onλ-module and “seed-expanding.” First, it chooses seeds as essential PPIs or edges with high edge clustering values. Then, it identifies protein complexes by expanding each seed to aλ-module. MCSE is suitable for large PPI networks because of its low time complexity. MCSE can identify overlapping protein complexes naturally because a protein can be visited by different seeds. MCSE uses the parameterλ_th to control the range of seed expanding and can detect a hierarchical organization of protein complexes by tuning the value ofλ_th. Experimental results ofS. cerevisiaeshow that this hierarchical organization is similar to that of known complexes in MIPS database. The experimental results also show that MCSE outperforms other previous competing algorithms, such as CPM, CMC, Core-Attachment, Dpclus, HC-PIN, MCL, and NFC, in terms of the functional enrichment and matching with known protein complexes.

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Use of Network Pharmacology and Molecular Docking Technology to Analyze the Mechanism of Action of Velvet Antler in the Treatment of Postmenopausal Osteoporosis

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/7144529 ◽

2021 ◽

Vol 2021 ◽

pp. 1-22

Author(s):

Kuiting Guo ◽

Tiancheng Wang ◽

Enjing Luo ◽

Xiangyang Leng ◽

Baojin Yao

Keyword(s):

Postmenopausal Osteoporosis ◽

Mechanism Of Action ◽

Protein Interactions ◽

Target Genes ◽

Enrichment Analysis ◽

Mapk Signaling ◽

Network Pharmacology ◽

Protein Protein Interaction ◽

Pathway Annotation ◽

Velvet Antler

Deer velvet antlers are the young horns of male deer that are not ossified and densely overgrown. Velvet antler and its preparations have been widely used in the treatment of postmenopausal osteoporosis (PMOP) in recent years, although its mechanism of action in the human body remains unclear. To screen the effective ingredients and targets of velvet antler in the treatment of PMOP using network pharmacology and to explore the potential mechanisms of velvet antler action in such treatments, we screened the active ingredients and targets of velvet antler in the BATMAN-TCM database. We also screened the relevant targets of PMOP in the GeneCards and OMIM databases and then compared the targets at the intersection of both velvet antler and PMOP. We used Cytoscape 3.7.2 software to construct a network diagram of “disease-drug-components-targets” and a protein-protein interaction (PPI) network through the STRING database and screened out the core targets; the R language was then used to analyze the shared targets between antler and PMOP for GO-enrichment analysis and KEGG pathway-annotation analysis. Furthermore, we used the professional software Maestro 11.1 to verify the predictive analysis based on network pharmacology. Hematoxylin-eosin (H&E) staining and micro-CT were used to observe the changes in trabecular bone tissue, further confirming the results of network pharmacological analysis. The potentially effective components of velvet antler principally include 17β-E2, adenosine triphosphate, and oestrone. These components act on key target genes such as AKT1, IL6, MAPK3, TP53, EGFR, SRC, and TNF and regulate the PI3K/Akt-signaling and MAPK-signaling pathways. These molecules participate in a series of processes such as cellular differentiation, apoptosis, metabolism, and inflammation and can ultimately be used to treat PMOP; they reflect the overall regulation, network regulation, and protein interactions.

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Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

The Scientific World JOURNAL ◽

10.1155/2014/523634 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

Lei Yang ◽

Xianglong Tang

Keyword(s):

Gene Ontology ◽

Protein Interactions ◽

Protein Complexes ◽

False Negative ◽

Biological Database ◽

Interacting Proteins ◽

Ppi Network ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Biological Meaning

Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

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A system biology approach based on metabolic biomarkers and protein–protein interactions for identifying pathways underlying schizophrenia and bipolar disorder

Scientific Reports ◽

10.1038/s41598-021-93653-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Md. Altaf-Ul-Amin ◽

Kazuhisa Hirose ◽

João V. Nani ◽

Lucas C. Porta ◽

Ljubica Tasic ◽

...

Keyword(s):

Bipolar Disorder ◽

Protein Interactions ◽

Diagnostic Methods ◽

Brain Diseases ◽

Ppi Network ◽

Protein Protein Interactions ◽

Clinical Interviews ◽

Protein Protein Interaction ◽

High Prevalence ◽

Metabolic Biomarkers

AbstractMental disorders (MDs), including schizophrenia (SCZ) and bipolar disorder (BD), have attracted special attention from scientists due to their high prevalence and significantly debilitating clinical features. The diagnosis of MDs is still essentially based on clinical interviews, and intensive efforts to introduce biochemical based diagnostic methods have faced several difficulties for implementation in clinics, due to the complexity and still limited knowledge in MDs. In this context, aiming for improving the knowledge in etiology and pathophysiology, many authors have reported several alterations in metabolites in MDs and other brain diseases. After potentially fishing all metabolite biomarkers reported up to now for SCZ and BD, we investigated here the proteins related to these metabolites in order to construct a protein–protein interaction (PPI) network associated with these diseases. We determined the statistically significant clusters in this PPI network and, based on these clusters, we identified 28 significant pathways for SCZ and BDs that essentially compose three groups representing three major systems, namely stress response, energy and neuron systems. By characterizing new pathways with potential to innovate the diagnosis and treatment of psychiatric diseases, the present data may also contribute to the proposal of new intervention for the treatment of still unmet aspects in MDs.

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The Interactions of Small Proline-rich Proteins With Late Cornified Envelope Proteins Are Involved in the Pathogenesis of Psoriasis

10.21203/rs.3.rs-850424/v1 ◽

2021 ◽

Author(s):

Siyu Tian ◽

Shuming Chen ◽

Yongyi Feng ◽

Yong Li

Keyword(s):

Gene Expression ◽

Correlation Analysis ◽

Gene Expression Profile ◽

Expression Profile ◽

Protein Interactions ◽

Functional Enrichment ◽

Ppi Network ◽

Protein Protein Interactions ◽

Expression Levels ◽

Cornified Envelope

Abstract Background: Psoriasis is a common cutaneous disease with many characteristics including inflammation and aberrant keratinocyte proliferation. However, the pathogenesis of psoriasis is not completely clear. Methods: We explore the differentially expressed genes (DEGs) in psoriasis by analyzing the gene expression profile obtained from the Gene Expression Omnibus (GEO) database. The DEGs were examined by gene ontology (GO) functional enrichment analysis and protein-protein interactions (PPI) network. Correlation analysis in R studio software analyzed the association of SPRR and LCE genes. The potential direct protein-protein interactions between SPRR proteins and LCE3D was further verified by co-localization observed in 293T cells and co-immunoprecipitation (CO-IP). The expression levels of SPRR and LCE genes were detected in the IMQ-induced psoriasiform dermatitis mice. Results: The small proline-rich (SPRR) and late cornified envelope (LCE) genes were identified as a module in constructed PPI network. The gene expression profile GSE63684 analysis showed that both SPRR family and LCE family genes were significantly upregulated in imiquimod (IMQ) induced psoriasiform dermatitis mice. Correlation analysis in R studio software recognized the association of SPRR and LCE genes, in which the potential direct protein-protein interactions between SPRR proteins and LCE3D was further verified by co-localization observed in 293T cells and co-immunoprecipitation (CO-IP) results that suggest direct interaction between SPRR2 and LCE3D. Notably, we found that the expression levels of SPRR and LCE genes were significantly increased in the IMQ-induced psoriasiform dermatitis mice while tazarotene cream treatment specifically decreased the mRNA expression of these genes, which indicated that the SPRR and LCEs were regulated simultaneously in psoriasis. Conclusion: Our studies found the interactions of SPRR proteins with LCE proteins, which may provide new insights into the pathogenesis of psoriasis.

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Drug Target Protein-Protein Interaction Networks: A Systematic Perspective

BioMed Research International ◽

10.1155/2017/1289259 ◽

2017 ◽

Vol 2017 ◽

pp. 1-13 ◽

Cited By ~ 15

Author(s):

Yanghe Feng ◽

Qi Wang ◽

Tengjiao Wang

Keyword(s):

Protein Interactions ◽

Drug Target ◽

Drug Targets ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Ppi Network ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Topological Features ◽

Protein Protein Interaction Networks

The identification and validation of drug targets are crucial in biomedical research and many studies have been conducted on analyzing drug target features for getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we investigated three main ways to understand the functional biomolecules based on the topological features of drug targets. There are no significant differences between targets and common proteins in the protein-protein interactions network, indicating the drug targets are neither hub proteins which are dominant nor the bridge proteins. According to some special topological structures of the drug targets, there are significant differences between known targets and other proteins. Furthermore, the drug targets mainly belong to three typical communities based on their modularity. These topological features are helpful to understand how the drug targets work in the PPI network. Particularly, it is an alternative way to predict potential targets or extract nontargets to test a new drug target efficiently and economically. By this way, a drug target’s homologue set containing 102 potential target proteins is predicted in the paper.

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An efficient transient assays system using Agrobacterium-mediated transformation of onion (Allium cepa) epidermal cells

Indian Journal of Genetics and Plant Breeding (The) ◽

10.31742/ijgpb.80.3.17 ◽

2020 ◽

Vol 80 (03) ◽

Author(s):

Yu-Miao Zhang ◽

Jun Wang ◽

Tao Wu

Keyword(s):

Subcellular Localization ◽

Protein Interaction ◽

Protein Interactions ◽

Epidermal Cells ◽

Cyclin Dependent Kinase ◽

Protein Subcellular Localization ◽

Protein Protein Interactions ◽

Efficient System ◽

Protein Protein Interaction ◽

Onion Epidermal Cells

In this study, the Agrobacterium infection medium, infection duration, detergent, and cell density were optimized. The sorghum-based infection medium (SbIM), 10-20 min infection time, addition of 0.01% Silwet L-77, and Agrobacterium optical density at 600 nm (OD600), improved the competence of onion epidermal cells to support Agrobacterium infection at >90% efficiency. Cyclin-dependent kinase D-2 (CDKD-2) and cytochrome c-type biogenesis protein (CYCH), protein-protein interactions were localized. The optimized procedure is a quick and efficient system for examining protein subcellular localization and protein-protein interaction.

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