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 in silico method to identify candidate drugs for treating COVID-19.
Highlights
T HE current pandemic of COVID-19 caused by infection of the new coronavirus strain SARS-CoV-2 is a severe public health problem that must be resolved as soon as possible
We previously demonstrated that genes selected by tensor decomposition (TD)-based unsupervised feature extraction (FE) are useful to screen for drugs that are effective in treating disease or those that may cause adverse effects [15]
We applied a TD-based unsupervised FE method to select genes with altered expression caused by mouse hepatitis virus (MHV) infection in mice
Summary
T HE current pandemic of COVID-19 caused by infection of the new coronavirus strain SARS-CoV-2 is a severe public health problem that must be resolved as soon as possible. To achieve this goal, it is essential to understand the mechanism by which SARS-CoV-2 successfully invades human cells. There are many in silico trials for repositioning drugs toward COVID-19 [1]–[3], most of them are to try to find compounds that bind to SARS-CoV-2 proteins with in silico method. We previously identified drug candidate compounds using gene expression profiles of diseases [4], Manuscript received June 16, 2020; revised December 6, 2020 and February 9, 2021; accepted February 18, 2021.
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