Pervasive generation of non-canonical subgenomic RNAs by SARS-CoV-2

  1. Jason Nomburg
  2. Matthew Meyerson
  3. James A. DeCaprio

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Abstract

Background

SARS-CoV-2, a positive-sense RNA virus in the family Coronaviridae , has caused a worldwide pandemic of coronavirus disease 2019 or COVID-19. Coronaviruses generate a tiered series of subgenomic RNAs (sgRNAs) through a process involving homology between transcriptional regulatory sequences (TRS) located after the leader sequence in the 5′ UTR (the TRS-L) and TRS located near the start of ORFs encoding structural and accessory proteins (TRS-B) near the 3′ end of the genome. In addition to the canonical sgRNAs generated by SARS-CoV-2, non-canonical sgRNAs (nc-sgRNAs) have been reported. However, the consistency of these nc-sgRNAs across viral isolates and infection conditions is unknown. The comprehensive definition of SARS-CoV-2 RNA products is a key step in understanding SARS-CoV-2 pathogenesis.

Methods

Here, we report an integrative analysis of eight independent SARS-CoV-2 transcriptomes generated using three sequencing strategies, five host systems, and seven viral isolates. Read-mapping to the SARS-CoV-2 genome was used to determine the 5′ and 3′ coordinates of all junctions in viral RNAs identified in these samples.

Results

Using junctional abundances, we show nc-sgRNAs make up as much as 33% of total sgRNAs in cell culture models of infection, are largely consistent in abundance across independent transcriptomes, and increase in abundance over time during infection. By assessing the homology between sequences flanking the 5′ and 3′ junction points, we show that nc-sgRNAs are not associated with TRS-like homology. By incorporating read coverage information, we find strong evidence for subgenomic RNAs that contain only 5′ regions of ORF1a. Finally, we show that non-canonical junctions change the landscape of viral open reading frames.

Conclusions

We identify canonical and non-canonical junctions in SARS-CoV-2 sgRNAs and show that these RNA products are consistently generated by many independent viral isolates and sequencing approaches. These analyses highlight the diverse transcriptional activity of SARS-CoV-2 and offer important insights into SARS-CoV-2 biology.

Article activity feed

  1. Version published to 10.1186/s13073-020-00802-w
  2. Version published to 10.1101/2020.04.28.066951v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.04.28.066951: (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

    Software and Algorithms
    SentencesResources
    The exceptions are the transcriptomes from Finkel et al. and Emanuel et al. - because their read lengths were very short (60 and 72 bases in length respectively), mapping was conducted with STAR (31) using settings detailed in star.sh.
    STAR
    suggested: (STAR, RRID:SCR_015899)
    Analysis and visualization of junctions: The 5’ and 3’ coordinates of each junction were processed in R (32), and figures generated with ggplot2 (33).
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    (Command: samtools depth -aa -d0 file.bam > file.cov).
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    ORFs were called directly from each transcript using Prodigal (37), using parameters listed in prodigal.sh.
    Prodigal
    suggested: (Prodigal, RRID:SCR_011936)
    DIAMOND parameters are described in diamond.sh and include an E-value threshold of 10.
    DIAMOND
    suggested: (DIAMOND, RRID:SCR_009457)

    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.

  4. Version published to 10.1101/2020.04.28.066951v1 on bioRxiv