RNA-GPS Predicts SARS-CoV-2 RNA Residency to Host Mitochondria and Nucleolus

Authors

Kevin E Wu
Furqan M Fazal
Kevin R Parker
James Zou
Howard Y Chang

Publication Date

7-22-2020

Journal

Cell Systems

DOI

10.1016/j.cels.2020.06.008

PMID

32673562

PMCID

PMC7305881

PubMedCentral® Posted Date

7-22-2020

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Betacoronavirus, COVID-19, Cell Nucleolus, Coronavirus Infections, Databases, Genetic, Genome, Viral, Humans, Machine Learning, Mitochondria, Models, Genetic, Pandemics, Pneumonia, Viral, RNA, Viral, SARS-CoV-2, SARS-CoV-2, viral RNA localization, COX4, double-membrane vesicle, machine learning model, hypothesis generation, APEX-seq, proximity labelling

Abstract

SARS-CoV-2 genomic and subgenomic RNA (sgRNA) transcripts hijack the host cell's machinery. Subcellular localization of its viral RNA could, thus, play important roles in viral replication and host antiviral immune response. We perform computational modeling of SARS-CoV-2 viral RNA subcellular residency across eight subcellular neighborhoods. We compare hundreds of SARS-CoV-2 genomes with the human transcriptome and other coronaviruses. We predict the SARS-CoV-2 RNA genome and sgRNAs to be enriched toward the host mitochondrial matrix and nucleolus, and that the 5' and 3' viral untranslated regions contain the strongest, most distinct localization signals. We interpret the mitochondrial residency signal as an indicator of intracellular RNA trafficking with respect to double-membrane vesicles, a critical stage in the coronavirus life cycle. Our computational analysis serves as a hypothesis generation tool to suggest models for SARS-CoV-2 biology and inform experimental efforts to combat the virus. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.

Comments

Associated Data