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A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases

Background: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the etiological agent of coronavirus disease-2019 (COVID-19), is a novel Betacoronavirus that was first reported in Wuhan, China in December of 2019. The virus has since caused a worldwide pandemic that highlights the need to...

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Autores principales: Scott, Tiana M., Jensen, Sam, Pickett, Brett E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8607308/
https://www.ncbi.nlm.nih.gov/pubmed/34868553
http://dx.doi.org/10.12688/f1000research.52412.2
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author Scott, Tiana M.
Jensen, Sam
Pickett, Brett E.
author_facet Scott, Tiana M.
Jensen, Sam
Pickett, Brett E.
author_sort Scott, Tiana M.
collection PubMed
description Background: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the etiological agent of coronavirus disease-2019 (COVID-19), is a novel Betacoronavirus that was first reported in Wuhan, China in December of 2019. The virus has since caused a worldwide pandemic that highlights the need to quickly identify potential prophylactic or therapeutic treatments that can reduce the signs, symptoms, and/or spread of disease when dealing with a novel infectious agent. To combat this problem, we constructed a computational pipeline that uniquely combines existing tools to predict drugs and biologics that could be repurposed to combat an emerging pathogen. Methods: Our workflow analyzes RNA-sequencing data to determine differentially expressed genes, enriched Gene Ontology (GO) terms, and dysregulated pathways in infected cells, which can then be used to identify US Food and Drug Administration (FDA)-approved drugs that target human proteins within these pathways. We used this pipeline to perform a meta-analysis of RNA-seq data from cells infected with three Betacoronavirus species including severe acute respiratory syndrome coronavirus (SARS-CoV; SARS), Middle East respiratory syndrome coronavirus (MERS-CoV; MERS), and SARS-CoV-2, as well as respiratory syncytial virus and influenza A virus to identify therapeutics that could be used to treat COVID-19.  Results: This analysis identified twelve existing drugs, most of which already have FDA-approval, that are predicted to counter the effects of SARS-CoV-2 infection. These results were cross-referenced with interventional clinical trials and other studies in the literature to identify drugs on our list that had previously been identified or used as treatments for COIVD-19 including canakinumab, anakinra, tocilizumab, sarilumab, and baricitinib. Conclusions: While the results reported here are specific to Betacoronaviruses, such as SARS-CoV-2, our bioinformatics pipeline can be used to quickly identify candidate therapeutics for future emerging infectious diseases.
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spelling pubmed-86073082021-12-03 A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases Scott, Tiana M. Jensen, Sam Pickett, Brett E. F1000Res Method Article Background: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the etiological agent of coronavirus disease-2019 (COVID-19), is a novel Betacoronavirus that was first reported in Wuhan, China in December of 2019. The virus has since caused a worldwide pandemic that highlights the need to quickly identify potential prophylactic or therapeutic treatments that can reduce the signs, symptoms, and/or spread of disease when dealing with a novel infectious agent. To combat this problem, we constructed a computational pipeline that uniquely combines existing tools to predict drugs and biologics that could be repurposed to combat an emerging pathogen. Methods: Our workflow analyzes RNA-sequencing data to determine differentially expressed genes, enriched Gene Ontology (GO) terms, and dysregulated pathways in infected cells, which can then be used to identify US Food and Drug Administration (FDA)-approved drugs that target human proteins within these pathways. We used this pipeline to perform a meta-analysis of RNA-seq data from cells infected with three Betacoronavirus species including severe acute respiratory syndrome coronavirus (SARS-CoV; SARS), Middle East respiratory syndrome coronavirus (MERS-CoV; MERS), and SARS-CoV-2, as well as respiratory syncytial virus and influenza A virus to identify therapeutics that could be used to treat COVID-19.  Results: This analysis identified twelve existing drugs, most of which already have FDA-approval, that are predicted to counter the effects of SARS-CoV-2 infection. These results were cross-referenced with interventional clinical trials and other studies in the literature to identify drugs on our list that had previously been identified or used as treatments for COIVD-19 including canakinumab, anakinra, tocilizumab, sarilumab, and baricitinib. Conclusions: While the results reported here are specific to Betacoronaviruses, such as SARS-CoV-2, our bioinformatics pipeline can be used to quickly identify candidate therapeutics for future emerging infectious diseases. F1000 Research Limited 2021-08-20 /pmc/articles/PMC8607308/ /pubmed/34868553 http://dx.doi.org/10.12688/f1000research.52412.2 Text en Copyright: © 2021 Scott TM et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method Article
Scott, Tiana M.
Jensen, Sam
Pickett, Brett E.
A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases
title A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases
title_full A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases
title_fullStr A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases
title_full_unstemmed A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases
title_short A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases
title_sort signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8607308/
https://www.ncbi.nlm.nih.gov/pubmed/34868553
http://dx.doi.org/10.12688/f1000research.52412.2
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