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

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. [Display omitted] •Application of a machine-learning model of RNA subcellular localization to SARS-CoV-2•Viral RNAs show residency signal for host mitochondria and nucleolus•Mitochondria prediction suggests viruses repurpose endogenous localization pathways•Predictions may be linked to vesicle formation and viral-host protein interactions Where the SARS-CoV-2 genome localizes inside human cells remains understudied but may regulate viral replication and host response. We use a machine-learning model to predict subcellular residency of the SARS-CoV-2 genome and its encoded transcripts, as well as for other coronaviruses. Our predictions suggest new hypotheses for SARS-CoV-2 mechanisms.