Exploring G and C-quadruplex structures as potential targets against the severe acute respiratory syndrome coronavirus 2

This article has been Reviewed by the following groups

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Abstract

In this paper we report the analysis of the 2019-nCoV genome and related viruses using an upgraded version of the open-source algorithm G4-iM Grinder. This version improves the functionality of the software, including an easy way to determine the potential biological features affected by the candidates found. The quadruplex definitions of the algorithm were optimized for 2019-nCoV. Using a lax quadruplex definition ruleset, which accepts amongst other parameters two residue G- and C-tracks, hundreds of potential quadruplex candidates were discovered. These sequences were evaluated by their in vitro formation probability, their position in the viral RNA, their uniqueness and their conservation rates (calculated in over three thousand different COVID-19 clinical cases and sequenced at different times and locations during the ongoing pandemic). These results were compared sequentially to other Coronaviridae members, other Group IV (+)ssRNA viruses and the entire realm. Sequences found in common with other species were further analyzed and characterized. Sequences with high scores unique to the 2019-nCoV were studied to investigate the variations amongst similar species. Quadruplex formation of the best candidates was then confirmed experimentally. Using NMR and CD spectroscopy, we found several highly stable RNA quadruplexes that may be suitable theranostic targets against the 2019-nCoV.

GRAPHICAL ABSTRACT

Article activity feed

  1. SciScore for 10.1101/2020.08.19.257493: (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
    GenomicFeatures (supplementary material, section 1).
    GenomicFeatures
    suggested: (GenomicFeatures, RRID:SCR_016960)
    The reference information (including DOI and/or PubMedID) of each sequence is also listed and accessible to facilitate further studies.
    PubMedID
    suggested: (GeneDB Tbrucei, RRID:SCR_004786)
    Then, we calculated the PQS and PiMS densities of each virus to allow a direct size-independent comparison between them all (Figure 1, D), and filtered the results by their in vitro probability of formation score.
    PiMS
    suggested: (PiMS, RRID:SCR_011816)
    All other viral genomes used were retrieved from the NCBI database.
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    The genomic pairwise alignments, used to study the similarity between viruses and detect PQS and PiMS variations between species, were done using the package Biostrings from the Bioconductor repository.
    Bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    Spectra were acquired on Bruker Avance spectrometers operating at 600 MHz, and processed with Topspin software.
    Topspin
    suggested: (TopSpin, RRID:SCR_014227)

    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.