Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission

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Abstract

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases 1–3 . SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing 4,5 . Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

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  1. SciScore for 10.1101/2021.12.21.21268143: (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
    Miniaturized wastewater SARS-CoV-2 amplicon sequencing: The Swift Normalase® Amplicon Panels (SNAP) kit (PN: SN-5X296 (core) COVG1V2-96 (amplicon primers)
    SNAP
    suggested: (SNAP, RRID:SCR_007936)
    Between 5uL and 0.2uL of library material, depending on the data provided from the MiSeq Nano run, was pipetted into a single pool for the NovaSeq run.
    MiSeq
    suggested: (A5-miseq, RRID:SCR_012148)
    Sequencing depth and single nucleotide variant (SNV) calls are obtained using samtools mpileup25 and the iVar variants method18.
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    Lineage defining mutations are obtained from the UShER global phylogenetic tree using the matUtils package13.
    matUtils
    suggested: None
    Constrained minimization is performed in Python using the cvxpy convex optimization package26,27.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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.
    • Thank you for including a protocol registration statement.

    Results from scite Reference Check: We found no unreliable references.


    About SciScore

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