Emergence of the novel SARS-CoV-2 lineage P.4.1 and massive spread of P.2 in South Brazil

  1. Fernando Hayashi Sant’Anna
  2. Ana Paula Muterle Varela
  3. Janira Prichula
  4. Juliana Comerlato
  5. Carolina Baldisserotto Comerlato
  6. Vinicius Serafini Roglio
  7. Gerson Fernando Mendes Pereira
  8. Flávia Moreno
  9. Adriana Seixas
  10. Eliana Márcia Wendland

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Abstract

South Brazil has been the novel epicenter of Coronavirus Disease 2019 (COVID-19) in 2021, accounting for the greatest number of cumulative cases and deaths (per 100 thousand inhabitants in a week) worldwide. In this study, we analyzed 340 whole genomes of SARS-CoV-2, which were sampled between April and November 2020 in 33 cities in South Brazil. We demonstrated the circulation of two novel emergent lineages, described here as P.4 and P.4.1 (provisionally termed VUI-NP13L), and seven lineages that had already been assigned (B.1.1.33, B.1.1.28, P.2, B.1.91, B.1.1.94, B.1.195 and B.1.212). P.2 and P.4.1 demonstrated massive spread from approximately September/October 2020. Constant and consistent genomic surveillance is crucial to identify newly emerging SARS-CoV-2 lineages in Brazil and to guide decision making in the Brazilian Public Healthcare System.

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  1. ScreenIT

    SciScore for 10.1101/2021.04.14.21255429: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Bioethics, sample collection and processing: The study was approved by the Institutional Review Board of Moinhos de Vento Hospital under protocol number 32149620.9.0000.5330.
    Sex as a biological variablenot detected.
    RandomizationIn each of the 132 strata (33 weeks and 4 regions), samples were selected randomly for sequencing, totaling 353 RNA samples (Supplementary Methods). 3.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The reads were mapped with BWA 0.7.17 software33 to the Wuhan-Hu-1 reference genome (NC_045512.2) and converted to BAM format using samtools v1.734.
    BWA
    suggested: (BWA, RRID:SCR_010910)
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    1.2.3 package, and consensus sequences were generated considering a Phred quality score minimum of 20 and N for regions with coverage depths less than 10 bases.
    Phred
    suggested: (Phred, RRID:SCR_001017)
    Sequences were filtered using Augur Filter subcommand with a minimal length of 27,000 nucleotides, and subsequently aligned with MAFFT, FFT-NS-2 option with default parameters.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    The phylogenetic tree was built using IQ-Tree using the GTR model and ultrafast bootstrapping with 1,000 replicates, and subsequently visualized in iTOL v635. 6.
    IQ-Tree
    suggested: (IQ-TREE, RRID:SCR_017254)
    Kernel density of the lineages during the epidemiological weeks was built using a script written in Python (Seaborn library).
    Python
    suggested: (IPython, RRID:SCR_001658)

    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 10.1101/2021.04.14.21255429 on medRxiv
  3. Version published to 10.1080/22221751.2021.1949948