High-throughput Mutational Surveillance of the SARS-CoV-2 Spike Gene

  1. Ezgi Özkan
  2. Marcus Martin Strobl
  3. Maria Novatchkova
  4. Ramesh Yelagandula
  5. Tanino Guiseppe Albanese
  6. Petr Triska
  7. Lukas Endler
  8. Thomas Penz
  9. Timothej Patocka
  10. Vera Felsenstein
  11. Alexander Vogt
  12. Ido Tamir
  13. Tamara Seitz
  14. Manuela Födinger
  15. Ralf Herwig
  16. Alexander Indra
  17. Daniela Schmid
  18. Christoph Bock
  19. Andreas Bergthaler
  20. Alexander Stark
  21. Franz Allerberger
  22. Ulrich Elling
  23. Luisa Cochella

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Abstract

SARS-CoV-2 has evolved rapidly towards higher infectivity and partial immune escape over the course of the pandemic. This evolution is driven by the enormous virus population, that has infected close to 200 million people by now. Therefore, cost effective and scalable methods are needed to monitor viral evolution globally. Mutation-specific PCR approaches have become inadequate to distinguish the variety of circulating SARS-CoV-2 variants and are unable to detect novel ones. Conversely, whole genome sequencing protocols remain too labor- and cost-intensive to monitor SARS-CoV-2 at the required density. By adapting SARSeq we present a simple, fast, and scalable S-gene tiling pipeline for focused sequencing of the S-gene encoding for the spike protein. This method reports on all sequence positions with known importance for infectivity and immunity, yet scales to >20K samples per run. S-gene tiling is used for nationwide surveillance of SARS-CoV-2 at a density of 10% to 50% of all cases of infection in Austria. SARSeq S-tiling uncovered several infection clusters with variants of concern such as the biggest known cluster of Beta/B.1.351 outside Africa and successfully informed public health measures in a timely manner, allowing their successful implementation. Our close monitoring of mutations further highlighted evolutionary constraints and freedom of the spike protein ectodomain and sheds light on foreseeable evolutionary trajectories.

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    SciScore for 10.1101/2021.07.22.21259587: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The present study was approved by the local Ethics Committee of Vienna (#EK 21-141-0721) and the Federal Office for Safety and Health Care (
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    After alignment, we retained only reads that mapped in proper pairs using samtools v1.10. a textual pileup of the filtered alignment was obtained using samtools mpileup 1.10 using parameters “-aa -- max-depth 0 --no-BAQ --min-MQ 1” and further summarized using readstomper.pl and R v3.6.3.
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    BWA-MEM (v. 0.7.17)49 was used to map read pairs were to both the human (Hg38) and the SARS-CoV-2 genome (GenBank: MN908947.3, RefSeq: NC_045512.2) with a minimal seed length of 17.
    BWA-MEM
    suggested: (Sniffles, RRID:SCR_017619)

    Results from OddPub: Thank you for sharing your code and data.

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2021.07.22.21259587v1 on medRxiv