Rapid genomic surveillance of SARS-CoV-2 in a dense urban community using environmental (sewage) samples

  1. Rajindra Napit
  2. Prajwol Manandhar
  3. Ashok Chaudhary
  4. Bishwo Shrestha
  5. Ajit Poudel
  6. Roji Raut
  7. Saman Pradhan
  8. Samita Raut
  9. Sujala Mathema
  10. Rajesh Rajbhandari
  11. Sameer Dixit
  12. Jessica S. Schwind
  13. Christine K Johnson
  14. Jonna K Mazet
  15. Dibesh Karmacharya

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Abstract

Understanding disease burden and transmission dynamics in resource-limited, developing countries like Nepal is often challenging due to a lack of adequate surveillance systems. These issues are exacerbated by limited access to diagnostic and research facilities throughout the country. Nepal has one of the highest COVID-19 case rates (915 cases per 100,000 people) in South Asia, with densely-populated Kathmandu experiencing the highest number of cases. Swiftly identifying case clusters and introducing effective intervention programs is crucial to mounting an effective containment strategy. The rapid identification of circulating SARS-CoV-2 variants can also provide important information on viral evolution and epidemiology. Genomic-based environmental surveillance can help in the early detection of outbreaks before clinical cases are recognized, and identify viral micro-diversity that can be used for designing real-time risk-based interventions. This research aimed to develop a genomic-based environmental surveillance system by detecting and characterizing SARS-CoV-2 in sewage samples of Kathmandu using portable next-generation DNA sequencing devices. Out of 20 selected sites in the Kathmandu Valley, sewage samples from 16 (80%) sites had detectable SARS-CoV-2. A heat-map was created to visualize transmission activity in the community based on viral load intensity and corresponding geospatial data. Further, 41 mutations were observed in the SARS-CoV-2 genome. Some detected mutations (n=9, 2%) were novel and yet to be reported in the global database, with one indicating a frameshift deletion in the spike gene. We also observed more transition than transversion on detected mutations, indicating rapid viral evolution in the host. Our study has demonstrated the feasibility of rapidly obtaining vital information on community transmission and disease dynamics of SARS-CoV-2 using genomic-based environmental surveillance.

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  1. ScreenIT

    SciScore for 10.1101/2021.03.29.21254053: (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
    Nanoplot v1.30.1 [40] was used to check the quality of the raw nanopore fastq reads, and adapter sequences were trimmed using Porechop v0.2.4 [41].
    Porechop
    suggested: (Porechop, RRID:SCR_016967)
    The cleaned sequence reads were then de-novo assembled using Canu v2.0 [43] to generate contigs with an overlap parameter threshold of 100bp.
    Canu
    suggested: (Canu, RRID:SCR_015880)
    The scaffolds generated were subjected to the Basic Local Alignment Search Tool (BLAST) [44] for taxonomic identification against locally downloaded GenBank database (Release 240, October 15 2020).
    BLAST
    suggested: (BLASTX, RRID:SCR_001653)
    Whole-genome sequencing of SARS-CoV-2 using MinION: Bioinformatics workflow for consensus genome sequence generation: MinKNOW software (Oxford Nanopore, UK) was used to monitor sequencing run, collect the raw data, and perform real-time base calling.
    MinKNOW
    suggested: None
    The RAMPART v1.2.0 [46] software package developed by the ARTIC network was used to visualize read mapping and genome coverage in real-time for each barcode.
    RAMPART
    suggested: (Rampart, RRID:SCR_016742)
    Data analysis was done using the ARTIC pipeline v1.1.3 [47] of the ARTIC network’s nanopore bioinformatics protocol [48].
    ARTIC
    suggested: None
    The reads were mapped against the SARS-CoV-2 reference (NC_045512/MN908947) using the Minimap2 v2.17 [49].
    Minimap2
    suggested: (Minimap2, RRID:SCR_018550)
    The aligned read files were sorted using SAMtools v1.11 [50] to obtain coverage data and a consensus sequence.
    SAMtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    Whole-genome sequencing of SARS-CoV-2 using MiSeq: Using identical ARTIC amplicons, the first two positive samples from the study were sequenced in MiSeq (Illumina, USA) platform.
    MiSeq
    suggested: (A5-miseq, RRID:SCR_012148)

    Results from OddPub: Thank you for sharing your 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.
    • 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.

  2. Version published to 10.1101/2021.03.29.21254053v1 on medRxiv