Emergence and phenotypic characterization of the global SARS-CoV-2 C.1.2 lineage

  1. Cathrine Scheepers
  2. Josie Everatt
  3. Daniel G. Amoako
  4. Houriiyah Tegally
  5. Constantinos Kurt Wibmer
  6. Anele Mnguni
  7. Arshad Ismail
  8. Boitshoko Mahlangu
  9. Bronwen E. Lambson
  10. Darren P. Martin
  11. Eduan Wilkinson
  12. James Emmanuel San
  13. Jennifer Giandhari
  14. Nelia Manamela
  15. Noxolo Ntuli
  16. Prudence Kgagudi
  17. Sandile Cele
  18. Simone I. Richardson
  19. Sureshnee Pillay
  20. Thabo Mohale
  21. Upasana Ramphal
  22. Yeshnee Naidoo
  23. Zamantungwa T. Khumalo
  24. Gaurav Kwatra
  25. Glenda Gray
  26. Linda-Gail Bekker
  27. Shabir A. Madhi
  28. Vicky Baillie
  29. Wesley C. Van Voorhis
  30. Florette K. Treurnicht
  31. Marietjie Venter
  32. Koleka Mlisana
  33. Nicole Wolter
  34. Alex Sigal
  35. Carolyn Williamson
  36. Nei-yuan Hsiao
  37. Nokukhanya Msomi
  38. Tongai Maponga
  39. Wolfgang Preiser
  40. Zinhle Makatini
  41. Richard Lessells
  42. Penny L. Moore
  43. Tulio de Oliveira
  44. Anne von Gottberg
  45. Jinal N. Bhiman

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Abstract

Global genomic surveillance of SARS-CoV-2 has identified variants associated with increased transmissibility, neutralization resistance and disease severity. Here we report the emergence of the PANGO lineage C.1.2, detected at low prevalence in South Africa and eleven other countries. The initial C.1.2 detection is associated with a high substitution rate, and includes changes within the spike protein that have been associated with increased transmissibility or reduced neutralization sensitivity in SARS-CoV-2 variants of concern or variants of interest. Like Beta and Delta, C.1.2 shows significantly reduced neutralization sensitivity to plasma from vaccinees and individuals infected with the ancestral D614G virus. In contrast, convalescent donors infected with either Beta or Delta show high plasma neutralization against C.1.2. These functional data suggest that vaccine efficacy against C.1.2 will be equivalent to Beta and Delta, and that prior infection with either Beta or Delta will likely offer protection against C.1.2.

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  1. Version published to 10.1038/s41467-022-29579-9
  2. ScreenIT

    SciScore for 10.1101/2021.08.20.21262342: (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

    Software and Algorithms
    SentencesResources
    Assembly, processing and quality control of genomic sequences: Raw reads from Illumina sequencing were assembled using the Exatype NGS SARS-CoV-2 pipeline v1.6.1, (https://sars-cov-2.exatype.com/) or Genome Detective 1.132/1.133 (https://www.genomedetective.com/) and the Coronavirus Typing Tool42,43.
    SARS-CoV-2
    suggested: (BioLegend Cat# 946101, RRID:AB_2892515)
    A reference-based assembly and mapping was generated for each sample using Minimap2 and consensus calculated using Nanopolish.
    Nanopolish
    suggested: (Nanopolish, RRID:SCR_016157)
    The initial assembly obtained was cleaned by aligning mapped reads to the references and filtering out low-quality mutations using the Geneious software v2021.0.3 (Biomatters).
    Geneious
    suggested: (Geneious, RRID:SCR_010519)
    The resulting consensus sequence was further manually polished by considering and correcting indels in homopolymer regions that break the open reading frame (probably sequencing errors) using Aliview v1.27, (http://ormbunkar.se/aliview/)45.
    Aliview
    suggested: (AliView, RRID:SCR_002780)
    We used the Pymol program (The PyMOL Molecular Graphics System, version 2.2.0) for visualization.
    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 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.

  3. Version published to 10.1101/2021.08.20.21262342 on medRxiv