Biomimetic Virus-like Particles as SARS-CoV-2 Positive Controls for RT-PCR Diagnostics

  1. Soo Khim Chan
  2. Pinyi Du
  3. Karole Ignacio
  4. Sanjay Metha
  5. Isabel G. Newton
  6. Nicole F. Steinmetz

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Abstract

Coronavirus disease 2019 (COVID-19) is a highly transmissible disease that has affected more than 90% of the countries worldwide. At least 17 million individuals have been infected, and some countries are still battling first or second waves of the pandemic. Nucleic acid tests, especially reverse-transcription polymerase chain reaction (RT-PCR), have become the workhorse for early detection of COVID-19 infection. Positive controls for the molecular assays have been developed to validate each test and to provide high accuracy. However, most available positive controls require cold-chain distribution and cannot serve as full-process control. To overcome these shortcomings, we report the production of biomimetic virus-like particles (VLPs) as SARS-CoV-2 positive controls. A SARS-CoV-2 detection module for RT-PCR was encapsidated into VLPs from a bacteriophage and a plant virus. The chimeric VLPs were obtained either by in vivo reconstitution and co-expression of the target detection module and coat proteins or by in vitro assembly of purified detection module RNA sequences and coat proteins. These VLP-based positive controls mimic SARS-CoV-2 packaged RNA while being non-infectious. Most importantly, we demonstrated that the positive controls are scalable, stable, and can serve broadly as controls, from RNA extraction to PCR in clinical settings.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.16.20213991: (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.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    N1, N2 and RP primer/probes were synthesized from Integrated DNA Technologies (Table S2).
    N2
    suggested: None
    Software and Algorithms
    SentencesResources
    Production of Qβ 1P-C19 and Qβ 2P-C19 VLPs: Plasmids were transformed into BL21 (DE3) competent E. coli cells (New England Biolabs®) and plated out on antibiotic containing plate.
    New England Biolabs®
    suggested: (New England Biolabs, RRID:SCR_013517)
    In vitro RNA transcription of SDM was performed with Thermo Fisher Scientific’s MEGAscript™ T7 Transcription kit and purified with Invitrogen™’s MEGAclear™ Transcription Clean-Up kit.
    Thermo Fisher Scientific’s
    suggested: None
    MEGAscript™
    suggested: None
    The solution was later analyzed with native agarose gel electrophoresis as mentioned previously and band intensity was analyzed by ImageJ software.
    ImageJ
    suggested: (ImageJ, RRID:SCR_003070)
    The gel was later removed from TE buffer and RNA was extracted using Thermo Scientific™ GeneJET Gel Extraction Kit according to manufacturer’s protocol with slight modification.
    Thermo Scientific™
    suggested: (Thermo Scientific Wellwash Wellwash, RRID:SCR_020569)
    The data was processed using QuantSoft™ version 1.7.4 software.
    QuantSoft™
    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.

  2. Version published to 10.1101/2020.10.16.20213991v1 on medRxiv