High-throughput sequencing of SARS-CoV-2 in wastewater provides insights into circulating variants

  1. Rafaela S. Fontenele
  2. Simona Kraberger
  3. James Hadfield
  4. Erin M. Driver
  5. Devin Bowes
  6. LaRinda A. Holland
  7. Temitope O.C. Faleye
  8. Sangeet Adhikari
  9. Rahul Kumar
  10. Rosa Inchausti
  11. Wydale K. Holmes
  12. Stephanie Deitrick
  13. Philip Brown
  14. Darrell Duty
  15. Ted Smith
  16. Aruni Bhatnagar
  17. Ray A. Yeager
  18. Rochelle H. Holm
  19. Natalia Hoogesteijn von Reitzenstein
  20. Elliott Wheeler
  21. Kevin Dixon
  22. Tim Constantine
  23. Melissa A. Wilson
  24. Efrem S. Lim
  25. Xiaofang Jiang
  26. Rolf U. Halden
  27. Matthew Scotch
  28. Arvind Varsani

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Abstract

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  1. Version published to 10.1016/j.watres.2021.117710
  2. ScreenIT

    SciScore for 10.1101/2021.01.22.21250320: (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
    10 μl of cDNA from each sample was used to generate Illumina sequencing libraries (92 libraries in total) with the Swift Nomalase® Amplicon SARS CoV-2 Panel (SNAP) and these were subsequently normalized, pooled and sequenced at Psomagen (USA) on an Illumina HiSeq 2500 sequencer (2×100 paired-end option on 1 lane in rapid mode). 2.4.
    SNAP
    suggested: (SNAP, RRID:SCR_007936)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Although there are several limitations to the analysis of wastewater-derived SARS-CoV-2 sequences, our analysis of SNV-based supported lineages revealed some interesting findings. From the 52 analysed wastewater samples, 15 SARS-CoV-2 lineages assigned by PANGOLIN (Rambaut et al., 2020a) were supported, with lineage B.1.5 being the most prominent for the wastewater-derived sequences. The B.1.5 lineage has been identified in clinical samples in 27 USA states. Our wastewater-derived sequence data suggests that B.1.5 may also be present in 6 additional states in the USA (Arizona, Colorado, Idaho, Kansas, Kentucky and New Jersey). In 17 of the 52 wastewater samples, there were up to two supported SARS-CoV-2 lineages that had not been detected in North American clinical samples, during the period of our wastewater collection, as of 17th June 2020 (Figure 4). These 17 samples were from the states of Arizona, Kentucky and Massachusetts (Figure 4B). In wastewater-derived sequences from Arizona, which represents the greatest proportion of samples, the observed circulating lineages based on clinical-derived sequences are well represented (Ladner et al., 2020), with an additional nine possible circulating lineages identified. Although wastewater-based SARS-CoV-2 sequence analysis does not provide the same level of genome confidence (and thus lineage assignment) as those from clinical samples, the wastewater-derived data can be used to identify possible circulating lineages and assess th...

    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.

    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 10.1101/2021.01.22.21250320v1 on medRxiv