Secondary structural ensembles of the SARS-CoV-2 RNA genome in infected cells

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Abstract

SARS-CoV-2 is a betacoronavirus with a single-stranded, positive-sense, 30-kilobase RNA genome responsible for the ongoing COVID-19 pandemic. Although population average structure models of the genome were recently reported, there is little experimental data on native structural ensembles, and most structures lack functional characterization. Here we report secondary structure heterogeneity of the entire SARS-CoV-2 genome in two lines of infected cells at single nucleotide resolution. Our results reveal alternative RNA conformations across the genome and at the critical frameshifting stimulation element (FSE) that are drastically different from prevailing population average models. Importantly, we find that this structural ensemble promotes frameshifting rates much higher than the canonical minimal FSE and similar to ribosome profiling studies. Our results highlight the value of studying RNA in its full length and cellular context. The genomic structures detailed here lay groundwork for coronavirus RNA biology and will guide the design of SARS-CoV-2 RNA-based therapeutics.

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  1. SciScore for 10.1101/2020.06.29.178343: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    RESOURCE AVAILABILITY: EXPERIMENTAL MODEL AND SUBJECT DETAILS: SARS-CoV-2 total viral RNA was extracted from Vero cells (ATCC CCL-81) cultured in DMEM (Gibco) supplemented with 10% FBS (Gibco) plated into 100 mm dishes and infected at a MOI of 0.01 with 2019-nCoV/USA-WA1/2020 (Passage 6).
    Vero
    suggested: None

    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.