Extensive Recombination-driven Coronavirus Diversification Expands the Pool of Potential Pandemic Pathogens

  1. Stephen A Goldstein
  2. Joe Brown
  3. Brent S Pedersen
  4. Aaron R Quinlan
  5. Nels C Elde

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

The ongoing SARS-CoV-2 pandemic is the third zoonotic coronavirus identified in the last 20 years. Enzootic and epizootic coronaviruses of diverse lineages also pose a significant threat to livestock, as most recently observed for virulent strains of porcine epidemic diarrhea virus (PEDV) and swine acute diarrhea-associated coronavirus (SADS-CoV). Unique to RNA viruses, coronaviruses encode a proofreading exonuclease (ExoN) that lowers point mutation rates to increase the viability of large RNA virus genomes, which comes with the cost of limiting virus adaptation via point mutation. This limitation can be overcome by high rates of recombination that facilitate rapid increases in genetic diversification. To compare the dynamics of recombination between related sequences, we developed an open-source computational workflow (IDPlot) that bundles nucleotide identity, recombination, and phylogenetic analysis into a single pipeline. We analyzed recombination dynamics among three groups of coronaviruses with noteworthy impacts on human health and agriculture: SARSr-CoV, Betacoronavirus-1, and SADSr-CoV. We found that all three groups undergo recombination with highly diverged viruses from undersampled or unsampled lineages, including in typically highly conserved regions of the genome. In several cases, no parental origin of recombinant regions could be found in genetic databases, demonstrating our shallow characterization of coronavirus diversity and expanding the genetic pool that may contribute to future zoonotic events. Our results also illustrate the limitations of current sampling approaches for anticipating zoonotic threats to human and animal health.

Article activity feed

  1. Version published to 10.1093/gbe/evac161
  2. Version published to 10.1101/2021.02.03.429646v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.02.03.429646: (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
    For recombination analysis we ran GARD [29] as an optional step, utilizing the multiple sequence alignment generated by MAFFT.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    Phylogenetic validation of breakpoints: Putative breakpoints were further tested by maximum-likelihood phylogenetic analysis using PhyML [60].
    PhyML
    suggested: (PhyML, RRID:SCR_014629)
    BLAST analysis: To identify the source of recombinant regions we used NCBI Blastn with default parameters, excluding the query sequence from the search.
    BLAST
    suggested: (BLASTX, RRID:SCR_001653)
    Blastn
    suggested: (BLASTN, RRID:SCR_001598)

    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:
    • 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.

  4. Version published to 10.1101/2021.02.03.429646v1 on bioRxiv