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Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic has infected over 10 million people globally with a relatively high mortality rate. There are many therapeutics undergoing clinical trials, but there is no effective vaccine or therapy for treatment thus far. After affected by the Severe A...

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Autores principales: Li, Fuhai, Michelson, Andrew P., Foraker, Randi, Zhan, Ming, Payne, Philip R. O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789899/
https://www.ncbi.nlm.nih.gov/pubmed/33413329
http://dx.doi.org/10.1186/s12911-020-01373-x
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author Li, Fuhai
Michelson, Andrew P.
Foraker, Randi
Zhan, Ming
Payne, Philip R. O.
author_facet Li, Fuhai
Michelson, Andrew P.
Foraker, Randi
Zhan, Ming
Payne, Philip R. O.
author_sort Li, Fuhai
collection PubMed
description BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic has infected over 10 million people globally with a relatively high mortality rate. There are many therapeutics undergoing clinical trials, but there is no effective vaccine or therapy for treatment thus far. After affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), molecular signaling pathways of host cells play critical roles during the life cycle of SARS-CoV-2. Thus, it is significant to identify the involved molecular signaling pathways within the host cells. Drugs targeting these molecular signaling pathways could be potentially effective for COVID-19 treatment. METHODS: In this study, we developed a novel integrative analysis approach to identify the related molecular signaling pathways within host cells, and repurposed drugs as potentially effective treatments for COVID-19, based on the transcriptional response of host cells. RESULTS: We identified activated signaling pathways associated with the infection caused SARS-CoV-2 in human lung epithelial cells through integrative analysis. Then, the activated gene ontologies (GOs) and super GOs were identified. Signaling pathways and GOs such as MAPK, JNK, STAT, ERK, JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were particularly activated. Based on the identified signaling pathways and GOs, a set of potentially effective drugs were repurposed by integrating the drug-target and reverse gene expression data resources. In addition to many drugs being evaluated in clinical trials, the dexamethasone was top-ranked in the prediction, which was the first reported drug to be able to significantly reduce the death rate of COVID-19 patients receiving respiratory support. CONCLUSIONS: The integrative genomics data analysis and results can be helpful to understand the associated molecular signaling pathways within host cells, and facilitate the discovery of effective drugs for COVID-19 treatment.
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spelling pubmed-77898992021-01-08 Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2 Li, Fuhai Michelson, Andrew P. Foraker, Randi Zhan, Ming Payne, Philip R. O. BMC Med Inform Decis Mak Research Article BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic has infected over 10 million people globally with a relatively high mortality rate. There are many therapeutics undergoing clinical trials, but there is no effective vaccine or therapy for treatment thus far. After affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), molecular signaling pathways of host cells play critical roles during the life cycle of SARS-CoV-2. Thus, it is significant to identify the involved molecular signaling pathways within the host cells. Drugs targeting these molecular signaling pathways could be potentially effective for COVID-19 treatment. METHODS: In this study, we developed a novel integrative analysis approach to identify the related molecular signaling pathways within host cells, and repurposed drugs as potentially effective treatments for COVID-19, based on the transcriptional response of host cells. RESULTS: We identified activated signaling pathways associated with the infection caused SARS-CoV-2 in human lung epithelial cells through integrative analysis. Then, the activated gene ontologies (GOs) and super GOs were identified. Signaling pathways and GOs such as MAPK, JNK, STAT, ERK, JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were particularly activated. Based on the identified signaling pathways and GOs, a set of potentially effective drugs were repurposed by integrating the drug-target and reverse gene expression data resources. In addition to many drugs being evaluated in clinical trials, the dexamethasone was top-ranked in the prediction, which was the first reported drug to be able to significantly reduce the death rate of COVID-19 patients receiving respiratory support. CONCLUSIONS: The integrative genomics data analysis and results can be helpful to understand the associated molecular signaling pathways within host cells, and facilitate the discovery of effective drugs for COVID-19 treatment. BioMed Central 2021-01-07 /pmc/articles/PMC7789899/ /pubmed/33413329 http://dx.doi.org/10.1186/s12911-020-01373-x Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Li, Fuhai
Michelson, Andrew P.
Foraker, Randi
Zhan, Ming
Payne, Philip R. O.
Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2
title Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2
title_full Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2
title_fullStr Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2
title_full_unstemmed Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2
title_short Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2
title_sort computational analysis to repurpose drugs for covid-19 based on transcriptional response of host cells to sars-cov-2
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789899/
https://www.ncbi.nlm.nih.gov/pubmed/33413329
http://dx.doi.org/10.1186/s12911-020-01373-x
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