A Negative Feedback Model to Explain Regulation of SARS-CoV-2 Replication and Transcription

  1. Xin Li
  2. Zhi Cheng
  3. Fang Wang
  4. Jia Chang
  5. Qiang Zhao
  6. Hao Zhou
  7. Chang Liu
  8. Jishou Ruan
  9. Guangyou Duan
  10. Shan Gao

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Abstract

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although a preliminary understanding of the replication and transcription of SARS-CoV-2 has recently emerged, their regulation remains unknown.

Results

By comprehensive analysis of genome sequence and protein structure data, we propose a negative feedback model to explain the regulation of CoV replication and transcription, providing a molecular basis of the “leader-to-body fusion” model. The key step leading to the proposal of our model was that the transcription regulatory sequence (TRS) motifs were identified as the cleavage sites of nsp15, a nidoviral RNA uridylate-specific endoribonuclease (NendoU). According to this model, nsp15 regulates the synthesis of subgenomic RNAs (sgRNAs), and genomic RNAs (gRNAs) by cleaving TRSs. The expression level of nsp15 controls the relative proportions of sgRNAs and gRNAs, which in turn change the expression level of nsp15 to reach equilibrium between the CoV replication and transcription.

Conclusion

The replication and transcription of CoVs are regulated by a negative feedback mechanism that influences the persistence of CoVs in hosts. Our findings enrich fundamental knowledge in the field of gene expression and its regulation, and provide new clues for future studies. One important clue is that nsp15 may be an important and ideal target for the development of drugs (e.g., uridine derivatives) against CoVs.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.23.263327: (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
    In the present study, three types of plasmids pEGFP-C1 (maintained in the lab of Hao Zhou), pSARS and pCoV-ba, and three types (Hela, HEK293 and HEK293T) of cell maintained in our lab were used for transfection.
    HEK293
    suggested: None
    HEK293T
    suggested: NCBI_Iran Cat# C498, RRID:CVCL_0063)
    Software and Algorithms
    SentencesResources
    The results were confirmed using Illumina RNA-seq data from the NCBI SRA database under the accession number SRP251618.
    NCBI SRA
    suggested: None
    Statistics and plotting were conducted using the software R v2.15.3 with the package ggplot2 [10].
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    After 12 hours (0 hour in figure 2B), transfection of 1 μg of plasmid into one well was performed using 3 μL PolyJet (SignaGen Laboratories, USA), following the manufacturer’s instructions.
    PolyJet
    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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 10.3389/fgene.2021.641445
  3. Version published to 10.21203/rs.3.rs-98615/v1 on Research Square
  4. Version published to 10.1101/2020.08.23.263327v2 on bioRxiv
  5. Version published to 10.1101/2020.08.23.263327v1 on bioRxiv