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The coding capacity of SARS-CoV-2

Author

Listed:
  • Yaara Finkel

    (Weizmann Institute of Science)

  • Orel Mizrahi

    (Weizmann Institute of Science)

  • Aharon Nachshon

    (Weizmann Institute of Science)

  • Shira Weingarten-Gabbay

    (Broad Institute of MIT and Harvard
    Harvard University)

  • David Morgenstern

    (The Nancy and Stephen Grand Israel National Center for Personalised Medicine, Weizmann Institute of Science)

  • Yfat Yahalom-Ronen

    (Israel Institute for Biological Research)

  • Hadas Tamir

    (Israel Institute for Biological Research)

  • Hagit Achdout

    (Israel Institute for Biological Research)

  • Dana Stein

    (Israel Institute for Biological Research)

  • Ofir Israeli

    (Israel Institute for Biological Research)

  • Adi Beth-Din

    (Israel Institute for Biological Research)

  • Sharon Melamed

    (Israel Institute for Biological Research)

  • Shay Weiss

    (Israel Institute for Biological Research)

  • Tomer Israely

    (Israel Institute for Biological Research)

  • Nir Paran

    (Israel Institute for Biological Research)

  • Michal Schwartz

    (Weizmann Institute of Science)

  • Noam Stern-Ginossar

    (Weizmann Institute of Science)

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 2019 (COVID-19) pandemic1. To understand the pathogenicity and antigenic potential of SARS-CoV-2 and to develop therapeutic tools, it is essential to profile the full repertoire of its expressed proteins. The current map of SARS-CoV-2 coding capacity is based on computational predictions and relies on homology with other coronaviruses. As the protein complement varies among coronaviruses, especially in regard to the variety of accessory proteins, it is crucial to characterize the specific range of SARS-CoV-2 proteins in an unbiased and open-ended manner. Here, using a suite of ribosome-profiling techniques2–4, we present a high-resolution map of coding regions in the SARS-CoV-2 genome, which enables us to accurately quantify the expression of canonical viral open reading frames (ORFs) and to identify 23 unannotated viral ORFs. These ORFs include upstream ORFs that are likely to have a regulatory role, several in-frame internal ORFs within existing ORFs, resulting in N-terminally truncated products, as well as internal out-of-frame ORFs, which generate novel polypeptides. We further show that viral mRNAs are not translated more efficiently than host mRNAs; instead, virus translation dominates host translation because of the high levels of viral transcripts. Our work provides a resource that will form the basis of future functional studies.

Suggested Citation

  • Yaara Finkel & Orel Mizrahi & Aharon Nachshon & Shira Weingarten-Gabbay & David Morgenstern & Yfat Yahalom-Ronen & Hadas Tamir & Hagit Achdout & Dana Stein & Ofir Israeli & Adi Beth-Din & Sharon Melam, 2021. "The coding capacity of SARS-CoV-2," Nature, Nature, vol. 589(7840), pages 125-130, January.
  • Handle: RePEc:nat:nature:v:589:y:2021:i:7840:d:10.1038_s41586-020-2739-1
    DOI: 10.1038/s41586-020-2739-1
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    Citations

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    Cited by:

    1. Thomas Kruse & Caroline Benz & Dimitriya H. Garvanska & Richard Lindqvist & Filip Mihalic & Fabian Coscia & Raviteja Inturi & Ahmed Sayadi & Leandro Simonetti & Emma Nilsson & Muhammad Ali & Johanna K, 2021. "Large scale discovery of coronavirus-host factor protein interaction motifs reveals SARS-CoV-2 specific mechanisms and vulnerabilities," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. Lauren Jelley & Jordan Douglas & Xiaoyun Ren & David Winter & Andrea McNeill & Sue Huang & Nigel French & David Welch & James Hadfield & Joep Ligt & Jemma L. Geoghegan, 2022. "Genomic epidemiology of Delta SARS-CoV-2 during transition from elimination to suppression in Aotearoa New Zealand," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    3. Debjit Khan & Fulvia Terenzi & GuanQun Liu & Prabar K. Ghosh & Fengchun Ye & Kien Nguyen & Arnab China & Iyappan Ramachandiran & Shruti Chakraborty & Jennifer Stefan & Krishnendu Khan & Kommireddy Vas, 2023. "A viral pan-end RNA element and host complex define a SARS-CoV-2 regulon," Nature Communications, Nature, vol. 14(1), pages 1-22, December.
    4. Sophie Marianne Korn & Karthikeyan Dhamotharan & Cy M. Jeffries & Andreas Schlundt, 2023. "The preference signature of the SARS-CoV-2 Nucleocapsid NTD for its 5’-genomic RNA elements," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    5. David Gomez-Zepeda & Danielle Arnold-Schild & Julian Beyrle & Arthur Declercq & Ralf Gabriels & Elena Kumm & Annica Preikschat & Mateusz Krzysztof Łącki & Aurélie Hirschler & Jeewan Babu Rijal & Chris, 2024. "Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    6. Tammy C. T. Lan & Matty F. Allan & Lauren E. Malsick & Jia Z. Woo & Chi Zhu & Fengrui Zhang & Stuti Khandwala & Sherry S. Y. Nyeo & Yu Sun & Junjie U. Guo & Mark Bathe & Anders Näär & Anthony Griffith, 2022. "Secondary structural ensembles of the SARS-CoV-2 RNA genome in infected cells," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    7. Ma’ayan Israeli & Yaara Finkel & Yfat Yahalom-Ronen & Nir Paran & Theodor Chitlaru & Ofir Israeli & Inbar Cohen-Gihon & Moshe Aftalion & Reut Falach & Shahar Rotem & Uri Elia & Ital Nemet & Limor Klik, 2022. "Genome-wide CRISPR screens identify GATA6 as a proviral host factor for SARS-CoV-2 via modulation of ACE2," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    8. Palash Sashittal & Chuanyi Zhang & Jian Peng & Mohammed El-Kebir, 2021. "Jumper enables discontinuous transcript assembly in coronaviruses," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    9. Ioanna Tzani & Marina Castro-Rivadeneyra & Paul Kelly & Lisa Strasser & Lin Zhang & Martin Clynes & Barry L. Karger & Niall Barron & Jonathan Bones & Colin Clarke, 2024. "Detection of host cell microprotein impurities in antibody drug products," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    10. Bin Shao & Jiawei Yan & Jing Zhang & Lili Liu & Ye Chen & Allen R. Buskirk, 2024. "Riboformer: a deep learning framework for predicting context-dependent translation dynamics," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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