Disease Waves of SARS-CoV-2 in Iran Closely Mirror Global Pandemic Trends

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Abstract

Background: Complete SARS-CoV-2 genome sequencing in the early phase of the outbreak in Iran showed two independent viral entries. Subsequently, as part of a genome surveillance project, we aimed to characterize the genetic diversity of SARS-CoV-2 in Iran over one year after emerging. Methods: We provided 319 SARS-CoV-2 whole-genome sequences used to monitor circulating lineages in March 2020-May 2021 time interval. Results: The temporal dynamics of major SARS-CoV-2 clades/lineages circulating in Iran is comparable to the global perspective and represent the 19A clade (B.4) dominating the first disease wave, followed by 20A (B.1.36), 20B (B.1.1.413), 20I (B.1.1.7), leading the second, third and fourth waves, respectively. We observed a mixture of circulating B.1.36, B.1.1.413, B.1.1.7 lineages in winter 2021, paralleled in a fading manner for B.1.36/B.1.1.413 and a growing rise for B.1.1.7, prompting the fourth outbreak. Entry of the Delta variant, leading to the fifth disease wave in summer 2021, was detected in April 2021. This study highlights three lineages as hallmarks of the SARS-CoV-2 outbreak in Iran; B4, dominating early periods of the epidemic, B.1.1.413 (B.1.1 with the combination of [D138Y-S477N-D614G] spike mutations) as a characterizing lineage in Iran, and the co-occurrence of [I100T-L699I] spike mutations in half of B.1.1.7 sequences mediating the fourth peak. It also designates the renowned combination of G and GR clades’ mutations as the top recurrent mutations. Conclusion: In brief, we provided a real-time and comprehensive picture of the SARS-CoV-2 genetic diversity in Iran and shed light on the SARS-CoV-2 transmission and circulation on the regional scale.

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  1. SciScore for 10.1101/2021.10.23.21265086: (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.
    RandomizationMost of the samples were collected randomly from different cities in Iran through the help of the Iranian Network for Research in Viral Diseases (INRVD) or from private laboratories.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Initially, FASTQ files were assessed by FastQC (Andrews 2010) and then processed and compared with two in-house pipelines simultaneously.
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    Fastp (Chen et al. 2018) and Cutadapt (Martin 2011) programs were applied for FASTQ pre-processing, followed by alignment to the NC_045512.2 using Bowtie2 (Langmead and Salzberg 2012) or Burrows-Wheeler Aligners (Li and Durbin 2009), keeping the high-quality reads mapped in proper pair.
    Bowtie2
    suggested: (Bowtie 2, RRID:SCR_016368)
    The filtered BAM files were used for assembly of consensus SARS-CoV-2 sequences with Samtools mpileup and Bcftools (Li et al. 2009).
    Samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    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.

    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.