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

Cell Systems
Kevin WuHoward Y Chang

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.

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Citations

Aug 9, 2020·Inflammation Research : Official Journal of the European Histamine Research Society ... [et Al.]·Santosh Shenoy
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Datasets Mentioned

BETA
GSE116008

Methods Mentioned

BETA
APEX-seq

Software Mentioned

scipy Python
GPS
statsmodels
RNA
BioPython library
MATLAB
PyTorch
seaborn
Reactome
scikit

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