Subgenomic and negative sense RNAs are not markers of active replication of SARS-CoV-2 in nasopharyngeal swabs

  1. Anthony Chamings
  2. Tarka Raj Bhatta
  3. Soren Alexandersen

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly in the global population since its emergence in humans in late 2019. Replication of SARS-CoV-2 is characterised by transcription and replication of genomic length RNA and shorter subgenomic RNAs to produce virus proteins and ultimately progeny virions. Here we explore the pattern of both genome-length and subgenomic RNAs and positive and negative strand SARS-CoV-2 RNAs in diagnostic nasopharyngeal swabs using sensitive probe based PCR assays as well as Ampliseq panels designed to target subgenomic RNAs. We successfully developed a multiplex PCR assay to simultaneously measure the relative amount of SARS-CoV-2 full length genomic RNA as well as subgenomic N gene and subgenomic ORF7a RNA. We found that subgenomic RNAs and both positive and negative strand RNA can be readily detected in swab samples taken up to 19 and 17 days post symptom onset respectively, and are strongly correlated with the amount of genomic length RNA present within a sample. Their detection and measurement is therefore unlikely to provide anymore insight into the stage of infection and potential infectivity of an individual beyond what can already be inferred from the total viral RNA load measured by routine diagnostic SARS-CoV-2 PCRs. Using both an original commercial and two custom SARS-CoV-2 Ampliseq mini-panels, we identified that both ORF7a and N gene subgenomic RNAs were consistently the most abundant subgenomic RNAs. We were also able to identify several non-canonical subgenomic RNAs, including one which could potentially be used to translate the ORF7b protein and others which could be used to translate ORF9b and the ORF N* which has arisen from a new transcription regulatory sequence recently created by mutations after SARS-CoV-2 jumped into people. SARS-CoV-2 genomic length and subgenomic length RNA’s were present in samples even if cellular RNA was degraded, further indicating that these molecules are likely protected from degradation by the membrane structures seen in SARS-CoV-2 infected cells.

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  1. Version published to 10.1101/2021.06.29.21259511v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2021.06.29.21259511: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The study complied with all relevant ethical regulations and has been approved by the Barwon Health Human Research Ethics Committee (Ref HREC 20/56) and all participants gave their informed consent.
    Consent: The study complied with all relevant ethical regulations and has been approved by the Barwon Health Human Research Ethics Committee (Ref HREC 20/56) and all participants gave their informed consent.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    This sample consisted of the supernatant from a 48 hour third passage of SARS-CoV-2 virus in Vero E6 cells and kindly supplied by the Australian Centre for Disease Preparedness.
    Vero E6
    suggested: RRID:CVCL_XD71)
    Software and Algorithms
    SentencesResources
    Mapping of the reads was performed by TMAP software included in the Ion torrent Server software suite 5.10.148.
    TMAP
    suggested: (TMAP, RRID:SCR_000687)
    Amplicons were mapped to the latest RefSeq human transcript of human genome hg19 (GRCh38) obtained from the Ion Ampliseq Designer [https://ampliseq.com] (Thermofisher Scientific) using the TMAP software as part of the Ion Torrent Server Suite 5.10.1.
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    The read count data from the samples was normalized using python to calculate the reads per million mapped reads (RPM) to the inflammatory targets, and the Matplotlib version 3.4.250, Pandas version 1.2.451 and Seaborn 0.11.152 python packages were then used to create cluster maps of the RPM per sample to look for patterns of gene expression in the data, and to create bar plots of the relative levels of expression in the samples.
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)
    python
    suggested: (IPython, RRID:SCR_001658)
    To highlight genes with potential differential expression between the groups, the Kruskal-Wallis test and Wilcoxon Rank-Sum test was applied via the statistical package within the SciPy python library (1.6.1)53.
    SciPy
    suggested: (SciPy, RRID:SCR_008058)
    Gene relationships were analysed using String v1155 and the statistical over-representation test in Panther Gene Ontology version 1656.
    String
    suggested: (STRING, RRID:SCR_005223)
    Panther Gene Ontology
    suggested: None

    Results from OddPub: Thank you for sharing your data.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.06.29.21259511v1 on medRxiv