Sequencing identifies multiple early introductions of SARS-CoV-2 to the New York City region

  1. Matthew T. Maurano
  2. Sitharam Ramaswami
  3. Paul Zappile
  4. Dacia Dimartino
  5. Ludovic Boytard
  6. André M. Ribeiro-dos-Santos
  7. Nicholas A. Vulpescu
  8. Gael Westby
  9. Guomiao Shen
  10. Xiaojun Feng
  11. Megan S. Hogan
  12. Manon Ragonnet-Cronin
  13. Lily Geidelberg
  14. Christian Marier
  15. Peter Meyn
  16. Yutong Zhang
  17. John Cadley
  18. Raquel Ordoñez
  19. Raven Luther
  20. Emily Huang
  21. Emily Guzman
  22. Carolina Arguelles-Grande
  23. Kimon V. Argyropoulos
  24. Margaret Black
  25. Antonio Serrano
  26. Melissa E. Call
  27. Min Jae Kim
  28. Brendan Belovarac
  29. Tatyana Gindin
  30. Andrew Lytle
  31. Jared Pinnell
  32. Theodore Vougiouklakis
  33. John Chen
  34. Lawrence H. Lin
  35. Amy Rapkiewicz
  36. Vanessa Raabe
  37. Marie I. Samanovic
  38. George Jour
  39. Iman Osman
  40. Maria Aguero-Rosenfeld
  41. Mark J. Mulligan
  42. Erik M. Volz
  43. Paolo Cotzia
  44. Matija Snuderl
  45. Adriana Heguy

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Abstract

Effective public response to a pandemic relies upon accurate measurement of the extent and dynamics of an outbreak. Viral genome sequencing has emerged as a powerful approach to link seemingly unrelated cases, and large-scale sequencing surveillance can inform on critical epidemiological parameters. Here, we report the analysis of 864 SARS-CoV-2 sequences from cases in the New York City metropolitan area during the COVID-19 outbreak in spring 2020. The majority of cases had no recent travel history or known exposure, and genetically linked cases were spread throughout the region. Comparison to global viral sequences showed that early transmission was most linked to cases from Europe. Our data are consistent with numerous seeds from multiple sources and a prolonged period of unrecognized community spreading. This work highlights the complementary role of genomic surveillance in addition to traditional epidemiological indicators.

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  1. Version published to 10.1101/gr.266676.120
  2. ScreenIT

    SciScore for 10.1101/2020.04.15.20064931: (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
    Libraries presumed more suitable for capture (generally, qPCR Ct value > 30) were enriched for SARS-CoV-2 genomic sequences using custom biotinylated DNA probe pools either from Twist Biosciences or Integrated DNA Technologies: In general, we pooled samples with similar Ct values and accounted for variations in parent library concentration, multiplexing up to 23 libraries per reaction.
    Twist Biosciences
    suggested: None
    Sequenced read processing: Reads were demultiplexed with Illumina bcl2fastq v2.20 requiring a perfect match to indexing barcode sequences.
    bcl2fastq
    suggested: (bcl2fastq , RRID:SCR_015058)
    Illumina sequencing adapters were trimmed with Trimmomatic v0.39 (Bolger et al. 2014).
    Trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    Reads were aligned using BWA v0.7.17 (Li and Durbin 2009) to a custom index containing human genome reference (GRCh38/hg38) including unscaffolded contigs and alternate references plus the reference SARS-CoV-2 genome (NC_045512.2, wuhCor1).
    BWA
    suggested: (BWA, RRID:SCR_010910)
    Presumed PCR duplicates were marked using samblaster v0.1.24 (Faust and Hall 2014).
    samblaster
    suggested: (SAMBLASTER, RRID:SCR_000468)
    Variants were called across all samples using bcftools v1.9: Raw pileups were filtered using Viral sequences were generated by applying VCF files to the reference sequence using ‘bcftools consensus’ with -m to mask sites below 20x with Ns, and -m N to mask sites of ambiguous genotypes with N. Geoplotting: The regional case heat map was generated using R v3.6.2 using the packages ggplot2 v3.3.0 for plotting, and sf v0.8 for geospatial data manipulation.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Sequences were aligned along with the reference genome using MAFFT v7.453 (Katoh and Standley 2013), and the resulting alignment was masked to remove 100 bp from the beginning, 50 from the end, and uninformative point mutations (positions 11083, 13402, 21575, 24389, 24390).
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    A maximum likelihood tree was estimated using IQ-TREE 1.6.1 using a HKY substitution model (Nguyen et al. 2015).
    IQ-TREE
    suggested: (IQ-TREE, RRID:SCR_017254)

    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.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.04.15.20064931 on medRxiv