Translational landscape of SARS-CoV-2 and infected cells. 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. Overall design: 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.

研究目的：众多病毒可操控宿主翻译机器，特异性阻断宿主mRNA的翻译过程。本研究旨在结合RNA测序（RNA-seq）与核糖体测序（ribo-seq，一种可有效反映蛋白质水平变化的强大替代指标），解析新型冠状病毒（SARS-CoV-2）及其感染细胞的翻译组学全景。

实验方法：分别以不同感染复数感染Vero E6细胞与原代人支气管上皮细胞，在感染早期与晚期多个阶段收集样本。并行处理样本以分别开展RNA-seq与Ribo-seq测序。对测序reads进行接头序列与核糖体RNA（rRNA）序列的过滤清洗。首先将clean reads比对至SARS-CoV-2基因组，随后分别比对至非洲绿猴基因组与人类基因组，比对工具采用STAR。比对后的reads通过公开可用软件及上传至GitHub的自定义脚本完成后续注释与数据处理。差异基因表达分析采用EdgeR工具完成，基因集富集分析则通过TcGSA实现。

实验结果：本研究生成了多套RNA-seq与Ribo-seq文库，涵盖SARS-CoV-2感染细胞的转录组与翻译组学全景。研究发现，众多趋化因子与细胞因子的转录水平显著上调，但在SARS-CoV-2感染细胞中其翻译过程受到特异性阻断。

研究结论：本研究首次对感染细胞内的病毒与宿主翻译组学全景进行了详细解析，证实先天免疫相关宿主mRNA的翻译过程受到特异性阻断。本研究报道的优化数据分析流程，可为其他研究场景下的翻译组谱分析研究提供标准化框架。

整体实验设计：以不同感染复数感染Vero E6细胞与原代人支气管上皮细胞，在感染早期与晚期多个阶段收集样本。并行处理样本以分别开展RNA-seq与Ribo-seq测序。对测序reads进行接头序列与核糖体RNA序列的过滤清洗。首先将clean reads比对至SARS-CoV-2基因组，随后分别比对至非洲绿猴基因组与人类基因组，比对工具采用STAR。比对后的reads通过公开可用软件及上传至GitHub的自定义脚本完成后续注释与数据处理。差异基因表达分析采用EdgeR工具完成，基因集富集分析则通过TcGSA实现。