SARS-CoV-2 genome evolution exposes early human adaptations

  1. Erik S. Wright
  2. Seema S. Lakdawala
  3. Vaughn S. Cooper

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Abstract

The set of mutations observed at the outset of the SARS-CoV-2 pandemic may illuminate how the virus will adapt to humans as it continues to spread. Viruses are expected to quickly acquire beneficial mutations upon jumping to a new host species. Advantageous nucleotide substitutions can be identified by their parallel occurrence in multiple independent lineages and are likely to result in changes to protein sequences. Here we show that SARS-CoV-2 is acquiring mutations more slowly than expected for neutral evolution, suggesting purifying selection is the dominant mode of evolution during the initial phase of the pandemic. However, several parallel mutations arose in multiple independent lineages and may provide a fitness advantage over the ancestral genome. We propose plausible reasons for several of the most frequent mutations. The absence of mutations in other genome regions suggests essential components of SARS-CoV-2 that could be the target of drug development. Overall this study provides genomic insights into how SARS-CoV-2 has adapted and will continue to adapt to humans.

SUMMARY

In this study we sought signals of evolution to identify how the SARS-CoV-2 genome has adapted at the outset of the COVID-19 pandemic. We find that the genome is largely undergoing purifying selection that maintains its ancestral sequence. However, we identified multiple positions on the genome that appear to confer an adaptive advantage based on their repeated evolution in independent lineages. This information indicates how SARS-CoV-2 will evolve as it diversifies in an increasing number of hosts.

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    SciScore for 10.1101/2020.05.26.117069: (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
    Genomes were aligned and a maximum likelihood tree was created using DECIPHER with the best fitting evolutionary model.
    DECIPHER
    suggested: (DECIPHER, RRID:SCR_006552)

    Results from OddPub: Thank you for sharing your code.

    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:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • 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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  2. Version published to 10.1101/2020.05.26.117069v1 on bioRxiv