Mutation rate of SARS-CoV-2 and emergence of mutators during experimental evolution

  1. Massimo Amicone
  2. Vítor Borges
  3. Maria João Alves
  4. Joana Isidro
  5. Líbia Zé-Zé
  6. Sílvia Duarte
  7. Luís Vieira
  8. Raquel Guiomar
  9. João Paulo Gomes
  10. Isabel Gordo

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Background and objectives

To understand how organisms evolve, it is fundamental to study how mutations emerge and establish. Here, we estimated the rate of mutation accumulation of SARS-CoV-2 in vitro and investigated the repeatability of its evolution when facing a new cell type but no immune or drug pressures.

Methodology

We performed experimental evolution with two strains of SARS-CoV-2, one carrying the originally described spike protein (CoV-2-D) and another carrying the D614G mutation that has spread worldwide (CoV-2-G). After 15 passages in Vero cells and whole genome sequencing, we characterized the spectrum and rate of the emerging mutations and looked for evidences of selection across the genomes of both strains.

Results

From the frequencies of the mutations accumulated, and excluding the genes with signals of selection, we estimate a spontaneous mutation rate of 1.3 × 10−6 ± 0.2 × 10−6 per-base per-infection cycle (mean across both lineages of SARS-CoV-2 ± 2SEM). We further show that mutation accumulation is larger in the CoV-2-D lineage and heterogeneous along the genome, consistent with the action of positive selection on the spike protein, which accumulated five times more mutations than the corresponding genomic average. We also observe the emergence of mutators in the CoV-2-G background, likely linked to mutations in the RNA-dependent RNA polymerase and/or in the error-correcting exonuclease protein.

Conclusions and implications

These results provide valuable information on how spontaneous mutations emerge in SARS-CoV-2 and on how selection can shape its genome toward adaptation to new environments.

Lay Summary: Each time a virus replicates inside a cell, errors (mutations) occur. Here, via laboratory propagation in cells originally isolated from the kidney epithelium of African green monkeys, we estimated the rate at which the SARS-CoV-2 virus mutates—an important parameter for understanding how it can evolve within and across humans. We also confirm the potential of its Spike protein to adapt to a new environment and report the emergence of mutators—viral populations where mutations occur at a significantly faster rate.

Article activity feed

  1. Version published to 10.1093/emph/eoac010
  2. Version published to 10.1101/2021.05.19.444774v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.05.19.444774: (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

    Experimental Models: Cell Lines
    SentencesResources
    A SARS-CoV-2 stock was produced by infecting Vero E6 cells (freshly grown for 24h) and incubating the cells for 72 h.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    Briefly, we performed raw reads quality analysis using FastQC v0.11.9 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc), followed by quality improvement using Trimmomatic v.
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    Trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    Reference-based mapping was performed against the Wuhan-Hu-1/2019 reference genome sequence (https://www.ncbi.nlm.nih.gov/nuccore/MN908947.3; NC_045512.2)(Wu et al., 2020) using the Burrow-Wheeler Aligner (BWA_MEM) v.0.7.12 (r1039) (http://bio-bwa.sourceforge.net/)(Li and Durbin, 2009) integrated in multisoftware tool Snippy (https://github.com/tseemann/snippy) available in INSaFLU.
    BWA_MEM
    suggested: None
    http://bio-bwa.sourceforge.net/
    suggested: (BWA, RRID:SCR_010910)
    Variant (SNP/indels) calling was performed over BAM files using LoFreq v.
    LoFreq
    suggested: (LoFreq, RRID:SCR_013054)
    The effect of mutations on genes and predicted protein sequences was determined using Ensembl Variant Effect Predictor (VEP) version 103.1 (https://github.com/Ensembl/ensembl-vep; available as a self-contained Docker image)(McLaren et al., 2016).
    Ensembl Variant Effect Predictor
    suggested: None
    Variant
    suggested: (VARIANT, RRID:SCR_005194)
    The algorithm was written in R (version 3.6.1) and the results analyzed in RStudio.
    RStudio
    suggested: (RStudio, RRID:SCR_000432)

    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.

    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.

  4. Version published to 10.1101/2021.05.19.444774v1 on bioRxiv