Predicting the mutational drivers of future SARS-CoV-2 variants of concern

  1. M. Cyrus Maher
  2. Istvan Bartha
  3. Steven Weaver
  4. Julia di Iulio
  5. Elena Ferri
  6. Leah Soriaga
  7. Florian A. Lempp
  8. Brian L. Hie
  9. Bryan Bryson
  10. Bonnie Berger
  11. David L. Robertson
  12. Gyorgy Snell
  13. Davide Corti
  14. Herbert W. Virgin
  15. Sergei L. Kosakovsky Pond
  16. Amalio Telenti

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Abstract

SARS-CoV-2 evolution threatens vaccine- and natural infection–derived immunity and the efficacy of therapeutic antibodies. To improve public health preparedness, we sought to predict which existing amino acid mutations in SARS-CoV-2 might contribute to future variants of concern. We tested the predictive value of features comprising epidemiology, evolution, immunology, and neural network–based protein sequence modeling and identified primary biological drivers of SARS-CoV-2 intrapandemic evolution. We found evidence that ACE2-mediated transmissibility and resistance to population-level host immunity has waxed and waned as a primary driver of SARS-CoV-2 evolution over time. We retroactively identified with high accuracy (area under the receiver operator characteristic curve = 0.92 to 0.97) mutations that will spread, at up to 4 months in advance, across different phases of the pandemic. The behavior of the model was consistent with a plausible causal structure where epidemiological covariates combine the effects of diverse and shifting drivers of viral fitness. We applied our model to forecast mutations that will spread in the future and characterize how these mutations affect the binding of therapeutic antibodies. These findings demonstrate that it is possible to forecast the driver mutations that could appear in emerging SARS-CoV-2 variants of concern. We validated this result against Omicron, showing elevated predictive scores for its component mutations before emergence and rapid score increase across daily forecasts during emergence. This modeling approach may be applied to any rapidly evolving pathogens with sufficiently dense genomic surveillance data, such as influenza, and unknown future pandemic viruses.

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  1. Version published to 10.1126/scitranslmed.abk3445
  2. ScreenIT

    SciScore for 10.1101/2021.06.21.21259286: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    G Epsilon, B.1.427 (additional associated mutations): S13I, W152C B.1.429 (CDC VOCs): S13I, W152C, L452R, D614G SARS-CoV-2 pseudotyped VSV production and neutralization: To generate SARS-CoV-2 pseudotyped vesicular stomatitis virus, Lenti-X 293T cells (Takara) were seeded in 10-cm dishes for 80%.
    293T
    suggested: None
    For viral neutralization, Vero E6 cells were seeded into black-walled, clear-bottom 96-well plates at 20,000 cells/well and cultured overnight at 37°C.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    Code availability and environment: Analyses on GISAID data extracts were conducted in python (Python Software Foundation.
    python
    suggested: (IPython, RRID:SCR_001658)
    Available at http://www.python.org).
    http://www.python.org
    suggested: (CVXOPT - Python Software for Convex Optimization, RRID:SCR_002918)
    Mutations were then extracted as compared to the reference with R 4.0.2 (https://www.r-project.org/) using Biostrings 2.56.0 (https://bioconductor.org/packages/Biostrings) and haplotypes were obtained by combining all amino acid mutations (substitutions, insertions, and deletions) identified on the Spike protein when compared to the reference sequence.
    https://www.r-project.org/
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)
    Biostrings
    suggested: (Biostrings, RRID:SCR_016949)
    Natural selection features were generated using MEME45 and FEL23 methods implemented in the HyPhy package24 (version 2.5.31).
    HyPhy
    suggested: (HyPhy, RRID:SCR_016162)

    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 did not find any issues relating to the usage of bar graphs.

    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.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.06.21.21259286v1 on medRxiv