The Heterogeneous Landscape and Early Evolution of Pathogen-Associated CpG Dinucleotides in SARS-CoV-2

  1. Andrea Di Gioacchino
  2. Petr Šulc
  3. Anastassia V Komarova
  4. Benjamin D Greenbaum
  5. Rémi Monasson
  6. Simona Cocco

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Abstract

COVID-19 can lead to acute respiratory syndrome, which can be due to dysregulated immune signaling. We analyze the distribution of CpG dinucleotides, a pathogen-associated molecular pattern, in the SARS-CoV-2 genome. We characterize CpG content by a CpG force that accounts for statistical constraints acting on the genome at the nucleotidic and amino acid levels. The CpG force, as the CpG content, is overall low compared with other pathogenic betacoronaviruses; however, it widely fluctuates along the genome, with a particularly low value, comparable with the circulating seasonal HKU1, in the spike coding region and a greater value, comparable with SARS and MERS, in the highly expressed nucleocapside coding region (N ORF), whose transcripts are relatively abundant in the cytoplasm of infected cells and present in the 3′UTRs of all subgenomic RNA. This dual nature of CpG content could confer to SARS-CoV-2 the ability to avoid triggering pattern recognition receptors upon entry, while eliciting a stronger response during replication. We then investigate the evolution of synonymous mutations since the outbreak of the COVID-19 pandemic, finding a signature of CpG loss in regions with a greater CpG force. Sequence motifs preceding the CpG-loss-associated loci in the N ORF match recently identified binding patterns of the zinc finger antiviral protein. Using a model of the viral gene evolution under human host pressure, we find that synonymous mutations seem driven in the SARS-CoV-2 genome, and particularly in the N ORF, by the viral codon bias, the transition–transversion bias, and the pressure to lower CpG content.

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  1. Version published to 10.1093/molbev/msab036
  2. Version published to 10.1101/2020.05.06.074039v4 on bioRxiv
  3. Version published to 10.1101/2020.05.06.074039v3 on bioRxiv
  4. Version published to 10.1101/2020.05.06.074039v2 on bioRxiv
  5. ScreenIT

    SciScore for 10.1101/2020.05.06.074039: (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
    Parameters affecting the force values: Parameters affecting the SMS: 4.6 Data Analysis: SARS-CoV-2 sequences are taken from GISAID [32].
    SMS
    suggested: None
    To produce the bar plots in Figs. 3c and 3b we collected genes data on VIPR.
    VIPR
    suggested: (vipR, RRID:SCR_010685)

    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.

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  6. Version published to 10.1101/2020.05.06.074039v1 on bioRxiv