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A Network-Based Approach to Explore the Mechanisms of Uncaria Alkaloids in Treating Hypertension and Alleviating Alzheimer’s Disease
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 biologi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7084279/ https://www.ncbi.nlm.nih.gov/pubmed/32143538 http://dx.doi.org/10.3390/ijms21051766 |
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author | Wu, Wenyong Zhang, Zijia Li, Feifei Deng, Yanping Lei, Min Long, Huali Hou, Jinjun Wu, Wanying |
author_facet | Wu, Wenyong Zhang, Zijia Li, Feifei Deng, Yanping Lei, Min Long, Huali Hou, Jinjun Wu, Wanying |
author_sort | Wu, Wenyong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7084279 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70842792020-03-24 A Network-Based Approach to Explore the Mechanisms of Uncaria Alkaloids in Treating Hypertension and Alleviating Alzheimer’s Disease Wu, Wenyong Zhang, Zijia Li, Feifei Deng, Yanping Lei, Min Long, Huali Hou, Jinjun Wu, Wanying Int J Mol Sci Article 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. MDPI 2020-03-04 /pmc/articles/PMC7084279/ /pubmed/32143538 http://dx.doi.org/10.3390/ijms21051766 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wu, Wenyong Zhang, Zijia Li, Feifei Deng, Yanping Lei, Min Long, Huali Hou, Jinjun Wu, Wanying A Network-Based Approach to Explore the Mechanisms of Uncaria Alkaloids in Treating Hypertension and Alleviating Alzheimer’s Disease |
title | A Network-Based Approach to Explore the Mechanisms of Uncaria Alkaloids in Treating Hypertension and Alleviating Alzheimer’s Disease |
title_full | A Network-Based Approach to Explore the Mechanisms of Uncaria Alkaloids in Treating Hypertension and Alleviating Alzheimer’s Disease |
title_fullStr | A Network-Based Approach to Explore the Mechanisms of Uncaria Alkaloids in Treating Hypertension and Alleviating Alzheimer’s Disease |
title_full_unstemmed | A Network-Based Approach to Explore the Mechanisms of Uncaria Alkaloids in Treating Hypertension and Alleviating Alzheimer’s Disease |
title_short | A Network-Based Approach to Explore the Mechanisms of Uncaria Alkaloids in Treating Hypertension and Alleviating Alzheimer’s Disease |
title_sort | network-based approach to explore the mechanisms of uncaria alkaloids in treating hypertension and alleviating alzheimer’s disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7084279/ https://www.ncbi.nlm.nih.gov/pubmed/32143538 http://dx.doi.org/10.3390/ijms21051766 |
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