A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases [version 2; peer review: 2 approved, 1 approved with reservations]

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:	Tiana M. Scott, Sam Jensen, Brett E. Pickett
Formato:	article
Lenguaje:	EN
Publicado:	F1000 Research Ltd 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/593270438fb7483794a24a92a666fbf9
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spelling	oai:doaj.org-article:593270438fb7483794a24a92a666fbf92021-11-22T12:41:06ZA signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases [version 2; peer review: 2 approved, 1 approved with reservations]2046-140210.12688/f1000research.52412.2https://doaj.org/article/593270438fb7483794a24a92a666fbf92021-08-01T00:00:00Zhttps://f1000research.com/articles/10-330/v2https://doaj.org/toc/2046-1402Background: 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.Tiana M. ScottSam JensenBrett E. PickettF1000 Research LtdarticleMedicineRScienceQENF1000Research, Vol 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tiana M. Scott Sam Jensen Brett E. Pickett A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases [version 2; peer review: 2 approved, 1 approved with reservations]
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.
format	article
author	Tiana M. Scott Sam Jensen Brett E. Pickett
author_facet	Tiana M. Scott Sam Jensen Brett E. Pickett
author_sort	Tiana M. Scott
title	A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases [version 2; peer review: 2 approved, 1 approved with reservations]
title_short	A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases [version 2; peer review: 2 approved, 1 approved with reservations]
title_full	A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases [version 2; peer review: 2 approved, 1 approved with reservations]
title_fullStr	A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases [version 2; peer review: 2 approved, 1 approved with reservations]
title_full_unstemmed	A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases [version 2; peer review: 2 approved, 1 approved with reservations]
title_sort	signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases [version 2; peer review: 2 approved, 1 approved with reservations]
publisher	F1000 Research Ltd
publishDate	2021
url	https://doaj.org/article/593270438fb7483794a24a92a666fbf9
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A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases [version 2; peer review: 2 approved, 1 approved with reservations]

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