Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC

  1. Katharina Jahn
  2. David Dreifuss
  3. Ivan Topolsky
  4. Anina Kull
  5. Pravin Ganesanandamoorthy
  6. Xavier Fernandez-Cassi
  7. Carola Bänziger
  8. Alexander J. Devaux
  9. Elyse Stachler
  10. Lea Caduff
  11. Federica Cariti
  12. Alex Tuñas Corzón
  13. Lara Fuhrmann
  14. Chaoran Chen
  15. Kim Philipp Jablonski
  16. Sarah Nadeau
  17. Mirjam Feldkamp
  18. Christian Beisel
  19. Catharine Aquino
  20. Tanja Stadler
  21. Christoph Ort
  22. Tamar Kohn
  23. Timothy R. Julian
  24. Niko Beerenwinkel

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Abstract

The continuing emergence of SARS-CoV-2 variants of concern and variants of interest emphasizes the need for early detection and epidemiological surveillance of novel variants. We used genomic sequencing of 122 wastewater samples from three locations in Switzerland to monitor the local spread of B.1.1.7 (Alpha), B.1.351 (Beta) and P.1 (Gamma) variants of SARS-CoV-2 at a population level. We devised a bioinformatics method named COJAC (Co-Occurrence adJusted Analysis and Calling) that uses read pairs carrying multiple variant-specific signature mutations as a robust indicator of low-frequency variants. Application of COJAC revealed that a local outbreak of the Alpha variant in two Swiss cities was observable in wastewater up to 13 d before being first reported in clinical samples. We further confirmed the ability of COJAC to detect emerging variants early for the Delta variant by analysing an additional 1,339 wastewater samples. While sequencing data of single wastewater samples provide limited precision for the quantification of relative prevalence of a variant, we show that replicate and close-meshed longitudinal sequencing allow for robust estimation not only of the local prevalence but also of the transmission fitness advantage of any variant. We conclude that genomic sequencing and our computational analysis can provide population-level estimates of prevalence and fitness of emerging variants from wastewater samples earlier and on the basis of substantially fewer samples than from clinical samples. Our framework is being routinely used in large national projects in Switzerland and the UK.

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  1. Version published to 10.1038/s41564-022-01185-x
  2. Version published to 10.1101/2021.01.08.21249379v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.01.08.21249379: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Frozen cDNA transcripts of RNA extracts from wastewater samples were analyzed using NGS.
    NGS
    suggested: (PM4NGS, RRID:SCR_019164)
    Low-frequency mutation calling was based on local haplotype reconstruction with ShoRAH (Zagordi et al. 2010a, 2010b, 2011; McElroy et al. 2013; Posada-Céspedes et al. 2020)
    ShoRAH
    suggested: (ShoRAH, RRID:SCR_005211)

    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.
    • Thank you for including a protocol registration statement.

    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 10.1101/2021.01.08.21249379v1 on medRxiv