Version 1
: Received: 23 April 2020 / Approved: 24 April 2020 / Online: 24 April 2020 (09:21:20 CEST)
Version 2
: Received: 16 June 2020 / Approved: 17 June 2020 / Online: 17 June 2020 (13:26:51 CEST)
How to cite:
Taguchi, Y.; Turki, T. Novel Method for Detection of Genes With Altered Expression Caused by Coronavirus Infection and Screening of Candidate Drugs for SARS-CoV-2. Preprints2020, 2020040431 (doi: 10.20944/preprints202004.0431.v2).
Taguchi, Y.; Turki, T. Novel Method for Detection of Genes With Altered Expression Caused by Coronavirus Infection and Screening of Candidate Drugs for SARS-CoV-2. Preprints 2020, 2020040431 (doi: 10.20944/preprints202004.0431.v2).
Cite as:
Taguchi, Y.; Turki, T. Novel Method for Detection of Genes With Altered Expression Caused by Coronavirus Infection and Screening of Candidate Drugs for SARS-CoV-2. Preprints2020, 2020040431 (doi: 10.20944/preprints202004.0431.v2).
Taguchi, Y.; Turki, T. Novel Method for Detection of Genes With Altered Expression Caused by Coronavirus Infection and Screening of Candidate Drugs for SARS-CoV-2. Preprints 2020, 2020040431 (doi: 10.20944/preprints202004.0431.v2).
Abstract
To better understand the genes with altered expression caused by infection with the novel coronavirus strain SARS-CoV-2 causing COVID-19 infectious disease, a tensor decomposition (TD)-based unsupervised feature extraction (FE) approach was applied to a gene expression profile dataset of the mouse liver and spleen with experimental infection of mouse hepatitis virus, which is regarded as a suitable model of human coronavirus infection. TD-based unsupervised FE selected 134 altered genes, which were enriched in protein-protein interactions with orf1ab, polyprotein, and 3C-like protease that are well known to play critical roles in coronavirus infection, suggesting that these 134 genes can represent the coronavirus infectious process. We then selected compounds targeting the expression of the 134 selected genes based on a public domain database. The identified drug compounds were mainly related to known antiviral drugs, several of which were also included in those previously screened with an \textit{in silico} method to identify candidate drugs for treating COVID-19.
Subject Areas
COVID-19; SARS-CoV-2; in silico drug discovery; gene expression profile; tensor decomposition; feature extraction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
17 June 2020
Commenter:
Y-H. Taguchi
Commenter's Conflict of Interests:
Author
Comment:
Revised based upon reviewers' comments.Two additional files of SARS-CoV infections to mouse lung are added.Comparision with SARS-CoV-2 proteins as well as drugs developed for SARS-CoV-2 were added.Several analysis using synthetic data is added
Y-H. Taguchi
Turki Turki
Commenter: Y-H. Taguchi
Commenter's Conflict of Interests: Author