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WeightNameValue
1000 Titel
  • RNA-GPS Predicts SARS-CoV-2 RNA Residency to Host Mitochondria and Nucleolus
1000 Autor/in
  1. Wu, Kevin E. |
  2. Fazal, Furqan M. |
  3. Parker, Kevin R. |
  4. Zou, James |
  5. Chang, Howard Y. |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-06-20
1000 Erschienen in
1000 Quellenangabe
  • 11(1):102-108.e3
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1016/j.cels.2020.06.008 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305881/ |
1000 Ergänzendes Material
  • https://www.sciencedirect.com/science/article/pii/S2405471220302374#appsec2 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 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.
1000 Sacherschließung
gnd 1206347392 COVID-19
lokal COX4
lokal viral RNA localization
lokal APEX-seq
lokal proximity labelling
lokal machine learning model
lokal double-membrane vesicle
lokal SARS-CoV-2
lokal hypothesis generation
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/V3UsIEtldmluIEUu|https://frl.publisso.de/adhoc/uri/RmF6YWwsIEZ1cnFhbiBNLg==|https://frl.publisso.de/adhoc/uri/UGFya2VyLCBLZXZpbiBSLg==|https://frl.publisso.de/adhoc/uri/Wm91LCBKYW1lcw==|https://frl.publisso.de/adhoc/uri/Q2hhbmcsIEhvd2FyZCBZLg==
1000 Label
1000 Förderer
  1. National Institutes of Health |
  2. Division of Computing and Communication Foundations |
  3. Silicon Valley Community Foundation |
  4. Chan Zuckerberg Initiative |
  5. National Human Genome Research Institute |
1000 Fördernummer
  1. RM1-HG007735; R01- HG004361; R21 MD012867-01; P30AG059307; U01MH098953
  2. 1763191
  3. -
  4. -
  5. HG010910
1000 Förderprogramm
  1. -
  2. -
  3. -
  4. -
  5. NIH K99/R00 award
1000 Dateien
  1. RNA-GPS Predicts SARS-CoV-2 RNA Residency to Host Mitochondria and Nucleolus
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer National Institutes of Health |
    1000 Förderprogramm -
    1000 Fördernummer RM1-HG007735; R01- HG004361; R21 MD012867-01; P30AG059307; U01MH098953
  2. 1000 joinedFunding-child
    1000 Förderer Division of Computing and Communication Foundations |
    1000 Förderprogramm -
    1000 Fördernummer 1763191
  3. 1000 joinedFunding-child
    1000 Förderer Silicon Valley Community Foundation |
    1000 Förderprogramm -
    1000 Fördernummer -
  4. 1000 joinedFunding-child
    1000 Förderer Chan Zuckerberg Initiative |
    1000 Förderprogramm -
    1000 Fördernummer -
  5. 1000 joinedFunding-child
    1000 Förderer National Human Genome Research Institute |
    1000 Förderprogramm NIH K99/R00 award
    1000 Fördernummer HG010910
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6422156.rdf
1000 Erstellt am 2020-07-27T09:55:34.399+0200
1000 Erstellt von 21
1000 beschreibt frl:6422156
1000 Bearbeitet von 25
1000 Zuletzt bearbeitet Thu Feb 25 09:38:42 CET 2021
1000 Objekt bearb. Thu Feb 25 09:38:41 CET 2021
1000 Vgl. frl:6422156
1000 Oai Id
  1. oai:frl.publisso.de:frl:6422156 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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