Virologica Sinica

Virologica Sinica

Volume 37, Issue 6, December 2022, Pages 813-822
Virologica Sinica

Research Article
Translation landscape of SARS-CoV-2 noncanonical subgenomic RNAs

https://doi.org/10.1016/j.virs.2022.09.003Get rights and content
Under a Creative Commons license
open access

Highlights

  • The first systematic large-scale study for identifying translational evidence of SARS-CoV-2 noncanonical sgRNAs.

  • We developed a general vipep pipeline for RNA viruses to analyze the mass spectrum and RNA-seq datasets.

  • Many peptides are translated from noncanonical sgRNAs and are dynamically regulated in the same manner as annotated proteins.

  • The novel noncanonical proteins may play important roles, such as binding to viral RNAs.

Abstract

The ongoing COVID-19 pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with a positive-stranded RNA genome. Current proteomic studies of SARS-CoV-2 mainly focus on the proteins encoded by its genomic RNA (gRNA) or canonical subgenomic RNAs (sgRNAs). Here, we systematically investigated the translation landscape of SARS-CoV-2, especially its noncanonical sgRNAs. We first constructed a strict pipeline, named vipep, for identifying reliable peptides derived from RNA viruses using RNA-seq and mass spectrometry data. We applied vipep to analyze 24 sets of mass spectrometry data related to SARS-CoV-2 infection. In addition to known canonical proteins, we identified many noncanonical sgRNA-derived peptides, which stably increase after viral infection. Furthermore, we explored the potential functions of those proteins encoded by noncanonical sgRNAs and found that they can bind to viral RNAs and may have immunogenic activity. The generalized vipep pipeline is applicable to any RNA viruses and these results have expanded the SARS-CoV-2 translation map, providing new insights for understanding the functions of SARS-CoV-2 sgRNAs.

Keywords

SARS-CoV-2
Subgenomic RNA (sgRNA)
Mass spectrometry
Translation
RNA binding

Cited by (0)

1

Kai Wu and Dehe Wang contributed equally to this work.