Differentiating between infectious and non-infectious influenza A virus and coronavirus RNA levels using long-range RT-qPCR

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Abstract

During the Coronavirus Disease 2019 (COVID-19) pandemic, residual SARS-CoV-2 genome and subgenomic RNA fragments were observed in recovered COVID-19 patients. The presence of such RNAs in the absence of live virus leads to incorrectly positive RT-qPCR results, potentially delaying medical procedures and quarantine release. We here propose a simple modification to turn commercial COVID-19 RT-qPCR protocols into long-range RT-qPCR assays that can differentiate between infectious and non-infectious influenza and coronavirus RNA levels. We find that the long-range RT-qPCR method has a sensitivity that is indistinguishable from a commercial Taq-Path COVID-19 RT-qPCR assay when tested on clinical samples taken withing 5 days of the onset of symptoms. In clinical samples taken at least 15 days after the onset of symptoms when patients had recovered from COVID-19, the modified RT-qPCR protocol leads to significantly fewer positive diagnoses. These findings suggest that the long-range RT-qPCR method may improve test-to-release protocols and expand the tools available for clinical COVID-19 diagnosis.

Importance

Various molecular tests can detect viral RNA in clinical samples. However, these molecular tests cannot differentiate between RNA from infectious viruses or residual viral genome fragments that are not infectious. In several percent of COVID-19 patients, such residual viral RNAs can be detected long after recovery and the disappearance of infectious SARS-CoV-2. These “persistently-positive” RT-qPCR results are different from false-positive RT-qPCR results, which can be generated due to in vitro cross-reactivity or contaminations. However, the detection of RNA fragments leads to incorrect conclusions about the status of a COVID-19 patient and an incorrect diagnosis. We here modified the commercial Taq-Path COVID-19 RT-qPCR kit to make this test less sensitive to residual viral RNA genome fragments, reducing the likelihood that incorrect RT-qPCR results affect the treatment or quarantine status of recovered COVID-19 patients.

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  1. SciScore for 10.1101/2021.11.11.21266219: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The investigation was approved by Vilnius Regional Bioethics Committee (approval number 2021/5-1342-818).
    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
    Influenza A/WSN/33 (H1N1) virus was produced by transfecting a 12-plasmid rescue system into HEK 293T cells (16).
    HEK 293T
    suggested: CCLV Cat# CCLV-RIE 1018, RRID:CVCL_0063)
    SARS-CoV-2 Bavpat-1 was grown on Vero-E6 cells in Dulbecco’s Minimal Essential Medium (DMEM; Gibco) containing 0.5% FBS at 37 °C and 5% CO2.
    Vero-E6
    suggested: None
    After virus adsorption to the MDCK cells, the inoculum was removed and replaced with 2 ml MEM/agarose overlay (MEM, 0.5% FBS, 1% agarose).
    MDCK
    suggested: CLS Cat# 602280/p823_MDCK_(NBL-2, RRID:CVCL_0422)
    Software and Algorithms
    SentencesResources
    Statistical testing was performed using GraphPad Prism 9.0.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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.

    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.