Design of a Companion Bioinformatic Tool to detect the emergence and geographical distribution of SARS-CoV-2 Spike protein genetic variants

  1. Alice Massacci
  2. Eleonora Sperandio
  3. Lorenzo D’Ambrosio
  4. Mariano Maffei
  5. Fabio Palombo
  6. Luigi Aurisicchio
  7. Gennaro Ciliberto
  8. Matteo Pallocca

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Abstract

Background

Tracking the genetic variability of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is a crucial challenge. Mainly to identify target sequences in order to generate robust vaccines and neutralizing monoclonal antibodies, but also to track viral genetic temporal and geographic evolution and to mine for variants associated with reduced or increased disease severity. Several online tools and bioinformatic phylogenetic analyses have been released, but the main interest lies in the Spike protein, which is the pivotal element of current vaccine design, and in the Receptor Binding Domain, that accounts for most of the neutralizing the antibody activity.

Methods

Here, we present an open-source bioinformatic protocol, and a web portal focused on SARS-CoV-2 single mutations and minimal consensus sequence building as a companion vaccine design tool. Furthermore, we provide immunogenomic analyses to understand the impact of the most frequent RBD variations.

Results

Results on the whole GISAID sequence dataset at the time of the writing (October 2020) reveals an emerging mutation, S477N, located on the central part of the Spike protein Receptor Binding Domain, the Receptor Binding Motif. Immunogenomic analyses revealed some variation in mutated epitope MHC compatibility, T-cell recognition, and B-cell epitope probability for most frequent human HLAs.

Conclusions

This work provides a framework able to track down SARS-CoV-2 genomic variability.

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  1. Version published to 10.1101/2020.06.22.133355v2 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.06.22.133355: (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
    Bioinformatic framework: Following the overall workflow (Figure 1A), a multi-FASTA file of viral sequences is aligned against the Wuhan strain (NC_045512.2) using NUCmer from the MUMmer package [
    Bioinformatic
    suggested: (QFAB Bioinformatics, RRID:SCR_012513)
    MUMmer
    suggested: (MUMmer, RRID:SCR_018171)
    Variant annotation is employed via the snpEff package [14] that embeds the NC_045512.2 genome assembly annotation in its standard package.
    snpEff
    suggested: (SnpEff, RRID:SCR_005191)
    A human-readable (and easily parsable) table is formatted thanks to the SnpSift jar package [15] of snpEff.
    SnpSift
    suggested: (SnpSift, RRID:SCR_015624)
    The backend is written in Python language by adopting the Flask web framework (version 1.1.2).
    Python
    suggested: (IPython, RRID:SCR_001658)

    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: We detected the following sentences addressing limitations in the study:
    As a limitation, we acknowledge that the consensus sequence generated by our workflow does not represent any particular clade nor viral isolate and does not take into account linkage and clustering among variations. However, the focus on specific mutational events can enable easier constant tracking for a virus that is undergoing millions of replications for clinical severity and vaccine efficacy monitoring.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

  3. Version published to 10.1101/2020.06.22.133355v1 on bioRxiv