From Alpha to Omicron BA.2: New digital RT-PCR approach and challenges for SARS-CoV-2 VOC monitoring and normalization of variant dynamics in wastewater

  1. Sebastien Wurtzer
  2. Morgane Levert
  3. Eloïse Dhenain
  4. Heberte Accrombessi
  5. Sandra Manco
  6. Nathalie Fagour
  7. Marion Goulet
  8. Nicolas Boudaud
  9. Lucie Gaillard
  10. Isabelle Bertrand
  11. Julie Challant
  12. Sophie Masnada
  13. Sam Azimi
  14. Miguel Gillon-Ritz
  15. Alban Robin
  16. Jean-Marie Mouchel
  17. Obepine Sig
  18. Laurent Moulin

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1016/j.scitotenv.2022.157740
  2. ScreenIT

    SciScore for 10.1101/2022.04.04.22273320: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Molecular quantification by digital RT-PCR: A panel of oligonucleotides was designed using AlleleID software vers. 7
    AlleleID
    suggested: (AlleleID, RRID:SCR_014790)
    Data analysis was performed using the Galaxy tools.
    Galaxy
    suggested: (Galaxy, RRID:SCR_006281)
    Reads were mapped with BWA-MEM (version 0.7.17.1) against reference genome (NC_45512).
    BWA-MEM
    suggested: (Sniffles, RRID:SCR_017619)
    Variant calling was performed using Lofreq (version 2.1.5) and annotation of SARS-CoV-2 variants using SnpEff eff version 4.5 covid19).
    Lofreq
    suggested: (LoFreq, RRID:SCR_013054)
    SnpEff
    suggested: (SnpEff, RRID:SCR_005191)
    Mathematical and statistical methods: Dynamics of mutations (concentration or mutation frequency) were modeled based on daily average concentration (when multiple locations were sampled on the same day) using a LOWESS smoothing method on 10 adjacent points (GraphPad Prism v9.0), allowing to limit outlier effects.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    First derivative of dynamics curves was calculated using dedicated function of GraphPad Prism software.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Variant identification results from sequencing, but such approach faces limitations for application in wastewater with respect to RNA quality and quantity (Lou et al., 2022), but also mathematical tools to deconvolute isolated mutations to reconstruct the original genomes in a mixture of variants (Agrawal et al., 2022b; Jahn et al., 2021; Smyth et al., 2022). Different studies have shown that it is also possible to monitor specific mutations suggestive of VOC to identify the emergence of VOC in a territory and to explain the predominance of certain VOC in epidemic waves (Caduff et al., 2022; Carcereny et al., 2022; Erster et al., 2022; Wurtzer et al., 2022). RT-qPCR approaches require calibration tools for absolute quantification or allelic discrimination (wild-type vs mutated) for relative quantification. Digital PCR allows more flexibility by offering a most probable number estimation (in accordance with Poisson’s law), by avoiding the realization of a standard curve and by allowing multiplexing of reactions. The del69-70 mutation was shared by Alpha and Omicron BA.1 VOCs. However, Alpha VOC has been chronologically replaced by Delta VOC, to the point of disappearance as confirmed by the absence of del69-70 mutation detection since summer 2021 in wastewater. In this regard, the WHO no longer identifies Alpha VOC as a circulating VOC (World Health Organization, 2022). On the other hand, Omicron BA.1 VOC carries the synonymous mutation C21762T, the presence of which confers a...

    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 10.1101/2022.04.04.22273320 on medRxiv