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

APEX-seq 570 Medical Sciences Histology Epidemiology Pneumonia, Viral Medical Immunology 610 proximity labelling Genome, Viral viral RNA localization Pathology and Forensic Medicine Machine Learning Databases Betacoronavirus Genetic Models Databases, Genetic Medical Specialties Medicine and Health Sciences Humans Clinical Epidemiology Viral Pandemics double-membrane vesicle COX4 Genome Models, Genetic SARS-CoV-2 Brief Report COVID-19 hypothesis generation Community Health and Preventive Medicine Pneumonia Cell Biology Mitochondria 3. Good health COVID-19 ; COX4 ; viral RNA localization ; APEX-seq ; proximity labelling ; machine learning model ; double-membrane vesicle ; SARS-CoV-2 ; hypothesis generation RNA RNA, Viral Public Health machine learning model Coronavirus Infections Cell Nucleolus
DOI: 10.1016/j.cels.2020.06.008 Publication Date: 2020-06-20T18:12:43Z
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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