Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses

  1. Ayal B. Gussow
  2. Noam Auslander
  3. Guilhem Faure
  4. Yuri I. Wolf
  5. Feng Zhang
  6. Eugene V. Koonin

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses an immediate, major threat to public health across the globe. Here we report an in-depth molecular analysis to reconstruct the evolutionary origins of the enhanced pathogenicity of SARS-CoV-2 and other coronaviruses that are severe human pathogens. Using integrated comparative genomics and machine learning techniques, we identify key genomic features that differentiate SARS-CoV-2 and the viruses behind the two previous deadly coronavirus outbreaks, SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), from less pathogenic coronaviruses. These features include enhancement of the nuclear localization signals in the nucleocapsid protein and distinct inserts in the spike glycoprotein that appear to be associated with high case fatality rate of these coronaviruses as well as the host switch from animals to humans. The identified features could be crucial contributors to coronavirus pathogenicity and possible targets for diagnostics, prognostication, and interventions.

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  1. Version published to 10.1073/pnas.2008176117
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    SciScore for 10.1101/2020.04.05.026450: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

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    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We then applied Support Vector Machines (using the Python library scikit-learn23 with a linear Kernel function) to a 5bp sliding window in the identified high-confidence alignment regions, with a leave-one-out cross validation (where all samples of one of the 7 coronaviruses were left out in each round of the cross validation, for a total of 7 rounds).
    Python
    suggested: (IPython, RRID:SCR_001658)
    Non-human proximal coronavirus strains: To compile a list of human and proximal non-human coronavirus strains, we first constructed a multiple sequence alignment of all 3001 collected strains using Mafft v7.407.
    Mafft
    suggested: (MAFFT, RRID:SCR_011811)
    Structural analyses including residues interactions and structural alignments were performed using the PyMOL computational framework30.
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.04.05.026450v2 on bioRxiv
  4. Version published to 10.1101/2020.04.05.026450v1 on bioRxiv