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New strategy for drug discovery by large-scale association analysis of molecular networks of different species
The development of modern omics technology has not significantly improved the efficiency of drug development. Rather precise and targeted drug discovery remains unsolved. Here a large-scale cross-species molecular network association (CSMNA) approach for targeted drug screening from natural sources...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4766474/ https://www.ncbi.nlm.nih.gov/pubmed/26912056 http://dx.doi.org/10.1038/srep21872 |
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author | Zhang, Bo Fu, Yingxue Huang, Chao Zheng, Chunli Wu, Ziyin Zhang, Wenjuan Yang, Xiaoyan Gong, Fukai Li, Yuerong Chen, Xiaoyu Gao, Shuo Chen, Xuetong Li, Yan Lu, Aiping Wang, Yonghua |
author_facet | Zhang, Bo Fu, Yingxue Huang, Chao Zheng, Chunli Wu, Ziyin Zhang, Wenjuan Yang, Xiaoyan Gong, Fukai Li, Yuerong Chen, Xiaoyu Gao, Shuo Chen, Xuetong Li, Yan Lu, Aiping Wang, Yonghua |
author_sort | Zhang, Bo |
collection | PubMed |
description | The development of modern omics technology has not significantly improved the efficiency of drug development. Rather precise and targeted drug discovery remains unsolved. Here a large-scale cross-species molecular network association (CSMNA) approach for targeted drug screening from natural sources is presented. The algorithm integrates molecular network omics data from humans and 267 plants and microbes, establishing the biological relationships between them and extracting evolutionarily convergent chemicals. This technique allows the researcher to assess targeted drugs for specific human diseases based on specific plant or microbe pathways. In a perspective validation, connections between the plant Halliwell-Asada (HA) cycle and the human Nrf2-ARE pathway were verified and the manner by which the HA cycle molecules act on the human Nrf2-ARE pathway as antioxidants was determined. This shows the potential applicability of this approach in drug discovery. The current method integrates disparate evolutionary species into chemico-biologically coherent circuits, suggesting a new cross-species omics analysis strategy for rational drug development. |
format | Online Article Text |
id | pubmed-4766474 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-47664742016-03-02 New strategy for drug discovery by large-scale association analysis of molecular networks of different species Zhang, Bo Fu, Yingxue Huang, Chao Zheng, Chunli Wu, Ziyin Zhang, Wenjuan Yang, Xiaoyan Gong, Fukai Li, Yuerong Chen, Xiaoyu Gao, Shuo Chen, Xuetong Li, Yan Lu, Aiping Wang, Yonghua Sci Rep Article The development of modern omics technology has not significantly improved the efficiency of drug development. Rather precise and targeted drug discovery remains unsolved. Here a large-scale cross-species molecular network association (CSMNA) approach for targeted drug screening from natural sources is presented. The algorithm integrates molecular network omics data from humans and 267 plants and microbes, establishing the biological relationships between them and extracting evolutionarily convergent chemicals. This technique allows the researcher to assess targeted drugs for specific human diseases based on specific plant or microbe pathways. In a perspective validation, connections between the plant Halliwell-Asada (HA) cycle and the human Nrf2-ARE pathway were verified and the manner by which the HA cycle molecules act on the human Nrf2-ARE pathway as antioxidants was determined. This shows the potential applicability of this approach in drug discovery. The current method integrates disparate evolutionary species into chemico-biologically coherent circuits, suggesting a new cross-species omics analysis strategy for rational drug development. Nature Publishing Group 2016-02-25 /pmc/articles/PMC4766474/ /pubmed/26912056 http://dx.doi.org/10.1038/srep21872 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Zhang, Bo Fu, Yingxue Huang, Chao Zheng, Chunli Wu, Ziyin Zhang, Wenjuan Yang, Xiaoyan Gong, Fukai Li, Yuerong Chen, Xiaoyu Gao, Shuo Chen, Xuetong Li, Yan Lu, Aiping Wang, Yonghua New strategy for drug discovery by large-scale association analysis of molecular networks of different species |
title | New strategy for drug discovery by large-scale association analysis of molecular networks of different species |
title_full | New strategy for drug discovery by large-scale association analysis of molecular networks of different species |
title_fullStr | New strategy for drug discovery by large-scale association analysis of molecular networks of different species |
title_full_unstemmed | New strategy for drug discovery by large-scale association analysis of molecular networks of different species |
title_short | New strategy for drug discovery by large-scale association analysis of molecular networks of different species |
title_sort | new strategy for drug discovery by large-scale association analysis of molecular networks of different species |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4766474/ https://www.ncbi.nlm.nih.gov/pubmed/26912056 http://dx.doi.org/10.1038/srep21872 |
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