ABSTRACT
BACKGROUND INFORMATION: Comprehensive libraries of plasmids for SARS-CoV-2 proteins with various tags (e.g., Strep, HA, Turbo) are now available. They enable the identification of numerous potential protein-protein interactions between the SARS-CoV-2 virus and host proteins. RESULTS: We present here a large library of SARS CoV-2 protein constructs fused with green and red fluorescent proteins and their initial characterisation in various human cell lines including lung epithelial cell models (A549, BEAS-2B), as well as in budding yeast. The localisation of a few SARS-CoV-2 proteins matches their proposed interactions with host proteins. These include the localisation of Nsp13 to the centrosome, Orf3a to late endosomes and Orf9b to mitochondria. CONCLUSIONS AND SIGNIFICANCE: This library should facilitate further cellular investigations, notably by imaging techniques.
Subject(s)
COVID-19/virology , Peptide Library , SARS-CoV-2/metabolism , Viral Proteins/metabolism , A549 Cells , Cell Line , Green Fluorescent Proteins/genetics , Green Fluorescent Proteins/metabolism , Host Microbial Interactions/physiology , Humans , Luminescent Proteins/genetics , Luminescent Proteins/metabolism , Microscopy, Fluorescence , Protein Interaction Domains and Motifs , Recombinant Fusion Proteins/genetics , Recombinant Fusion Proteins/metabolism , SARS-CoV-2/genetics , Saccharomyces cerevisiae/genetics , Saccharomyces cerevisiae/metabolism , Time-Lapse Imaging , Viral Proteins/geneticsABSTRACT
A year after the initial outbreak, the COVID-19 pandemic caused by SARS-CoV-2 virus remains a serious threat to global health, while current treatment options are insufficient to bring major improvements. The aim of this study is to identify repurposable drug candidates with a potential to reverse transcriptomic alterations in the host cells infected by SARS-CoV-2. We have developed a rational computational pipeline to filter publicly available transcriptomic datasets of SARS-CoV-2-infected biosamples based on their responsiveness to the virus, to generate a list of relevant differentially expressed genes, and to identify drug candidates for repurposing using LINCS connectivity map. Pathway enrichment analysis was performed to place the results into biological context. We identified 37 structurally heterogeneous drug candidates and revealed several biological processes as druggable pathways. These pathways include metabolic and biosynthetic processes, cellular developmental processes, immune response and signaling pathways, with steroid metabolic process being targeted by half of the drug candidates. The pipeline developed in this study integrates biological knowledge with rational study design and can be adapted for future more comprehensive studies. Our findings support further investigations of some drugs currently in clinical trials, such as itraconazole and imatinib, and suggest 31 previously unexplored drugs as treatment options for COVID-19.