Analysis of SARS-CoV-2 known and novel subgenomic mRNAs in cell culture, animal model, and clinical samples using LeTRS, a bioinformatic tool to identify unique sequence identifiers

  1. Xiaofeng Dong
  2. Rebekah Penrice-Randal
  3. Hannah Goldswain
  4. Tessa Prince
  5. Nadine Randle
  6. I'ah Donovan-Banfield
  7. Francisco J Salguero
  8. Julia Tree
  9. Ecaterina Vamos
  10. Charlotte Nelson
  11. Jordan Clark
  12. Yan Ryan
  13. James P Stewart
  14. Malcolm G Semple
  15. J Kenneth Baillie
  16. Peter J M Openshaw
  17. Lance Turtle
  18. David A Matthews
  19. Miles W Carroll
  20. Alistair C Darby
  21. Julian A Hiscox

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a complex strategy for the transcription of viral subgenomic mRNAs (sgmRNAs), which are targets for nucleic acid diagnostics. Each of these sgmRNAs has a unique 5′ sequence, the leader–transcriptional regulatory sequence gene junction (leader–TRS junction), that can be identified using sequencing. High-resolution sequencing has been used to investigate the biology of SARS-CoV-2 and the host response in cell culture and animal models and from clinical samples. LeTRS, a bioinformatics tool, was developed to identify leader–TRS junctions and can be used as a proxy to quantify sgmRNAs for understanding virus biology. LeTRS is readily adaptable for other coronaviruses such as Middle East respiratory syndrome coronavirus or a future newly discovered coronavirus. LeTRS was tested on published data sets and novel clinical samples from patients and longitudinal samples from animal models with coronavirus disease 2019. LeTRS identified known leader–TRS junctions and identified putative novel sgmRNAs that were common across different mammalian species. This may be indicative of an evolutionary mechanism where plasticity in transcription generates novel open reading frames, which can then subject to selection pressure. The data indicated multiphasic abundance of sgmRNAs in two different animal models. This recapitulates the relative sgmRNA abundance observed in cells at early points in infection but not at late points. This pattern is reflected in some human nasopharyngeal samples and therefore has implications for transmission models and nucleic acid–based diagnostics. LeTRS provides a quantitative measure of sgmRNA abundance from sequencing data. This can be used to assess the biology of SARS-CoV-2 (or other coronaviruses) in clinical and nonclinical samples, especially to evaluate different variants and medical countermeasures that may influence viral RNA synthesis.

Article activity feed

  1. Version published to 10.1093/gigascience/giac045
  2. ScreenIT

    SciScore for 10.1101/2021.03.03.433753: (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
    The LeTRS was also tested with a combined Nanopore cDNA ARTIC v3 amplicon dataset of 7 published viral cell culture samples (barcode01-barcode07) 16, and a dataset from a published direct RNA Nanopore sequencing analysis Vero cells infected with SARS-CoV-2 or an uninfected negative control 2.
    Vero
    suggested: CLS Cat# 605372/p622_VERO, RRID:CVCL_0059)
    Software and Algorithms
    SentencesResources
    The generated raw FastQ files (2 x 250 bp) were trimmed to remove Illumina adapter sequences using Cutadapt v1.2.1 26.
    Cutadapt
    suggested: (cutadapt, RRID:SCR_011841)
    The reads were further trimmed to remove low quality bases, using Sickle v1.200 27 with a minimum window quality score of 20.
    Sickle
    suggested: (Sickle, RRID:SCR_006800)
    The LeTRS was also tested with a combined Nanopore cDNA ARTIC v3 amplicon dataset of 7 published viral cell culture samples (barcode01-barcode07) 16, and a dataset from a published direct RNA Nanopore sequencing analysis Vero cells infected with SARS-CoV-2 or an uninfected negative control 2.
    SARS-CoV-2
    suggested: (Active Motif Cat# 91351, RRID:AB_2847848)
    Sequencing data alignment and basic filtering: LeTRS controlled Hisat2 v2.1.0 28 to map the paired-end Illumina reads against the SARS-CoV-2 reference genome (NC_045512.2) with the default setting, and Minimap2 v2.1 29 to align the Nanopore cDNA reads and direct RNA-seq reads on the viral genome using Minimap2 with “–ax splice” and “-ax splice -uf -k14” parameters, respectively.
    Minimap2
    suggested: (Minimap2, RRID:SCR_018550)
    LeTRS provided 10 known leader-TRS junctions to improve alignment accuracy by using “--known-splicesite-infile” function in Hisat2 and “--junc-bed” function in Minimap2, but this application could be optionally switched off by users.
    Hisat2
    suggested: (HISAT2, RRID:SCR_015530)
    In order to remove low mapping quality and mis-mapped reads before searching the leader-TRS junction sites, LeTRS used Samtools v1.9 30 to have basic filtering for the reads in the output Sam/Bam files according to their alignment states as shown (Table 9 - basic filtering).
    Samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    Leader-TRS junction plotting: LeTRS-plot was developed as an automatic plotting tool that interfaces with the R package ggplot2 v3.3.3 to view the leader-TRS junctions in the tables generated by LeTRS (Figure 3-5).
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    Results from OddPub: Thank you for sharing your code.

    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.

  3. Version published to 10.1101/2021.03.03.433753v2 on bioRxiv
  4. Version published to 10.1101/2021.03.03.433753v1 on bioRxiv