Translational landscape of SARS-CoV-2 and infected cells

Purpose: Numerous viruses manipulate the host translation machinery and specifically block host mRNA translation. The goal of this study is to define the translational landscape of SARS-CoV-2 and SARS-CoV-2 infected cells using a combination of RNA-seq and ribo-seq approches, the latter being a powerful proxy for protein-level changes. Methods: Vero E6 and primary human bronchial epithelial cells were infected with SARS-CoV-2 at varying multiplicities of infection and followed throughout early and late phases of infection. Parallel samples were processed for RNA-seq and Ribo-seq. Sequencing reads were cleaned off of adapters and rRNAs. Reads were first mapped to the SARS-CoV-2 genome and then to the African green monkey and human genomes using STAR. Mapped reads were further annotated and processed using publicly available software and custom scripts deposited at Github. DE gene expression analysis was performed usign EdgeR. Gene set enrichment analysis was done using TcGSA. Results: We provide the transcriptomic and translatomic landscape of SARS-CoV-2-infected cells from multiple RNA-seq and ribo-seq libraries. We found that the robust transcriptional upregulation of numerous chemokines and cytokines are translationally blocked in SARS-CoV-2-infected cells. Conclusions: Our study represents the first detailed analysis of viral and host translational landscape in infected cells and demonstrate that translation of host mRNAs involved in innate immunity is specifically blocked. The optimized data analysis workflows reported here should provide a framework for translational profiling studies in other settings. Vero E6 and primary human bronchial epithelial cells were infected with SARS-CoV-2 at varying multiplicities of infection and followed throughout early and late phases of infection. Parallel samples were processed for RNA-seq and Ribo-seq. Sequencing reads were cleaned off of adapters and rRNAs. Reads were first mapped to the SARS-CoV-2 genome and then to the African green monkey and human genomes using STAR. Mapped reads were further annotated and processed using publicly available software and custom scripts deposited at Github. DE gene expression analysis was performed usign EdgeR. Gene set enrichment analysis was done using TcGSA.