Variability in codon usage in Coronaviruses is mainly driven by mutational bias and selective constraints on CpG dinucleotide

This article has been Reviewed by the following groups

Read the full article

Abstract

The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the third virus within the Orthocoronavirinae causing an emergent infectious disease in humans, the ongoing coronavirus disease 2019 pandemic (COVID-19). Due to the high zoonotic potential of these viruses, it is critical to unravel their evolutionary history of host species shift, adaptation and emergence. Only such knowledge can guide virus discovery, surveillance and research efforts to identify viruses posing a pandemic risk in humans. We present a comprehensive analysis of the composition and codon usage bias of the 82 Orthocoronavirinae members, infecting 47 different avian and mammalian hosts. Our results clearly establish that synonymous codon usage varies widely among viruses and is only weakly dependent on the type of host they infect. Instead, we identify mutational bias towards AT-enrichment and selection against CpG dinucleotides as the main factors responsible of the codon usage bias variation. Further insight on the mutational equilibrium within Orthocoronavirinae revealed that most coronavirus genomes are close to their neutral equilibrium, the exception is the three recently-infecting human coronaviruses, which lie further away from the mutational equilibrium than their endemic human coronavirus counterparts. Finally, our results suggest that while replicating in humans SARS-CoV-2 is slowly becoming AT-richer, likely until attaining a new mutational equilibrium.

Article activity feed

  1. SciScore for 10.1101/2021.01.26.428296: (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

    No key resources detected.


    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.

    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.