Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Das, Soma
- Multi-Targeted Prediction of the Antiviral Effect of Momordica charantia extract based on Network Pharmacology
Abstract Views :120 |
PDF Views:80
Authors
Affiliations
1 Department of Pharmaceutical Sciences, School of Medical Sciences, Adamas University, Barasat-Barrackpore Road, Kolkata - 700126, West Bengal, IN
2 National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, Export Promotion Industrial Park (EPIP) Zandaha Road, NH322, Hajipur - 844102, Bihar, IN
3 Faculty of Pharmaceutical Science, Assam Down Town University, Sankar Madhav Path, Gandhinagar, Panikhaiti, Guwahati - 781026, Assam, IN
1 Department of Pharmaceutical Sciences, School of Medical Sciences, Adamas University, Barasat-Barrackpore Road, Kolkata - 700126, West Bengal, IN
2 National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, Export Promotion Industrial Park (EPIP) Zandaha Road, NH322, Hajipur - 844102, Bihar, IN
3 Faculty of Pharmaceutical Science, Assam Down Town University, Sankar Madhav Path, Gandhinagar, Panikhaiti, Guwahati - 781026, Assam, IN
Source
Journal of Natural Remedies, Vol 23, No 1 (2023), Pagination: 169-183Abstract
The fruits of Momordica charantia (Bitter Gourd) are well known for centuries as a natural remedy for the treatment of various ailments. In this study, we aimed to explore the metabolites present in both varieties of small and big bitter gourds and to explore the multitarget mechanism of M. charantia in antiviral infection by utilizing network pharmacology. The study design involves the identification of the compounds in both varieties of the bitter gourd by Agilent QTOFLC-MS/MS system, followed by screening for ADME to analyze the possible mechanism of action, disease association, protein-protein interactions and major pathways involved therein. Several Databases used were IMPAT, BindingDB, Swiss Target Prediction, STRING, DAVID, and KEGG databases, and algorithms were used to gather information. To visualize the network, Cytoscape 3.2.1 was used. As a result, a total of 22 and 27 compounds were detected from small and big bitter gourds respectively. . The molecules from M.charantia provide an antiviral response through the involvement of pathways like toll-like receptor pathway, PI3/AKT pathway, NF-kappa B signalling pathway, and cytokine-cytokine receptor interaction. Moreover, the core target genes termed ‘Hub Genes” were also identified through Cyto-hubba. The main mechanisms of M. charantia were acquired by investigating the enrichment of each cluster through functional association clustering analysis. Our results exposed the mechanism of M. charantia against viral infection by multi-component, multi-target, and multi-pathway study combinations.Keywords
Antiviral, Hub Genes, LC-MS, Momordica charantia, Network Pharmacology, Pathway Analysis.Full Text
References
- Ben-Shabat S, Yarmolinsky L, Porat D, et al. Antiviral effect of phytochemicals from medicinal plants: Applications and drug delivery strategies. Drug Deliv Transl Res. 2020; 10(2):354-367. https://doi. org/10.1007/s13346-019-00691-6 DOI: https://doi.org/10.1007/s13346-019-00691-6
- Abdel-Barry JA, Abdel-Hassan IA, Al-Hakiem MH. Hypoglycaemic and antihyperglycaemic effects of Trigonella foenum-graecum leaf in normal and alloxan induced diabetic rats. J Ethnopharmacol. 1997; 58(3):149-155. https://doi.org/10.1016/S0378- 8741(97)00101-3 DOI: https://doi.org/10.1016/S0378-8741(97)00101-3
- Beloin N, Gbeassor M, Akpagana K, et al. Ethnomedicinal uses of Momordica charantia (Cucurbitaceae) in Togo and relation to its phytochemistry and biological activity. J Ethnopharmacol. 2005; 96(1-2):49-55. https://doi. org/10.1016/j.jep.2004.08.009 DOI: https://doi.org/10.1016/j.jep.2004.08.009
- Zhang R, Zhu X, Bai H, et al. Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment. Front Pharmacol. 2019; 10. https:// doi.org/10.3389/fphar.2019.00123 DOI: https://doi.org/10.3389/fphar.2019.00123
- Yang Y, Yang K, Hao T, et al. Prediction of molecular mechanisms for lianxia ningxin formula: A network pharmacology study. Front Physiol. 2018; 9. https:// doi.org/10.3389/fphys.2018.00489 DOI: https://doi.org/10.3389/fphys.2018.00489
- Hopkins AL. Network Pharmacology. Nat Biotechnol. 2007; 25(10):1110-1111. https://doi.org/10.1038/ nbt1007-1110 DOI: https://doi.org/10.1038/nbt1007-1110
- Li S. Exploring traditional chinese medicine by a novel therapeutic concept of network target. Chin J Integr Med. 2016; 22(9):647-652. https://doi.org/10.1007/ s11655-016-2499-9 DOI: https://doi.org/10.1007/s11655-016-2499-9
- Huang J, Li L, Cheung F, et al. Network Pharmacology Based Approach to Investigate the Analgesic Efficacy and Molecular Targets of Xuangui Dropping Pill for Treating Primary Dysmenorrhea. Evid Based Complement Alternat Med. 2017; 2017. https://doi. org/10.1155/2017/7525179 DOI: https://doi.org/10.1155/2017/7525179
- Bader GD, Hogue CWV. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003; 4. https://doi.org/10.1186/1471-2105-4-2 DOI: https://doi.org/10.1186/1471-2105-4-2
- Rastogi R, Mehrotra B. Compendium of Indian medicinal plants. 1990.
- Ru J, Li P, Wang J, et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J Cheminformatics. 2014; 6(1). https:// doi.org/10.1186/1758-2946-6-13 DOI: https://doi.org/10.1186/1758-2946-6-13
- Bateman A, Martin MJ, O’Donovan C, et al. UniProt: The universal protein knowledgebase. Nucleic Acids Res. 2017; 45(D1):D158-D169. https://doi. org/10.1093/nar/gkw1099 DOI: https://doi.org/10.1093/nar/gkw1099
- Research UC-N acids, 2018 undefined. UniProt: the universal protein knowledge base. ncbi.nlm.nih.gov n.d.
- Fishilevich S, Zimmerman S, Kohn A, et al. Genic insights from integrated human proteomics in GeneCards. academic.oup.com n.d.
- Safran M, Dalah I, Alexander J, et al. GeneCards Version 3: the human gene integrator. Database J Biol Databases Curation. 2010; 2010. https://doi. org/10.1093/database/baq020 DOI: https://doi.org/10.1093/database/baq020
- Cline MS, Smoot M, Cerami E, et al. Integration of biological networks and gene expression data using cytoscape. Nat Protoc. 2007; 2(10):2366-2382. https:// doi.org/10.1038/nprot.2007.324 DOI: https://doi.org/10.1038/nprot.2007.324
- Maere S, Heymans K, Kuiper M. BiNGO: A Cytoscape plugin to assess over representation of Gene Ontology categories in Biological Networks. Bioinformatics. 2005; 21(16):3448-3449. https://doi.org/10.1093/ bioinformatics/bti551 DOI: https://doi.org/10.1093/bioinformatics/bti551
- Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome wide experimental datasets. Nucleic Acids Res. 2019; 47(D1):D607-D613. https://doi.org/10.1093/nar/ gky1131 DOI: https://doi.org/10.1093/nar/gky1131
- Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009; 4(1):44-57. https://doi.org/10.1038/nprot.2008.211 DOI: https://doi.org/10.1038/nprot.2008.211
- Shawky E. Prediction of potential cancer-related molecular targets of North African plants constituents using network pharmacology-based analysis. J Ethnopharmacol. 2019; 238:111826. https://doi. org/10.1016/j.jep.2019.111826 DOI: https://doi.org/10.1016/j.jep.2019.111826