Ribosome-Profiling Reveals Restricted Post Transcriptional Expression of Antiviral Cytokines and Transcription Factors during SARS-CoV-2 Infection

  1. Marina R. Alexander
  2. Aaron M. Brice
  3. Petrus Jansen van Vuren
  4. Christina L. Rootes
  5. Leon Tribolet
  6. Christopher Cowled
  7. Andrew G. D. Bean
  8. Cameron R. Stewart

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Abstract

The global COVID-19 pandemic caused by SARS-CoV-2 has resulted in over 2.2 million deaths. Disease outcomes range from asymptomatic to severe with, so far, minimal genotypic change to the virus so understanding the host response is paramount. Transcriptomics has become incredibly important in understanding host-pathogen interactions; however, post-transcriptional regulation plays an important role in infection and immunity through translation and mRNA stability, allowing tight control over potent host responses by both the host and the invading virus. Here, we apply ribosome profiling to assess post-transcriptional regulation of host genes during SARS-CoV-2 infection of a human lung epithelial cell line (Calu-3). We have identified numerous transcription factors (JUN, ZBTB20, ATF3, HIVEP2 and EGR1) as well as select antiviral cytokine genes, namely IFNB1, IFNL1,2 and 3, IL-6 and CCL5, that are restricted at the post-transcriptional level by SARS-CoV-2 infection and discuss the impact this would have on the host response to infection. This early phase restriction of antiviral transcripts in the lungs may allow high viral load and consequent immune dysregulation typically seen in SARS-CoV-2 infection.

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  1. Version published to 10.3390/ijms22073392
  2. ScreenIT

    SciScore for 10.1101/2021.03.03.433675: (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

    Antibodies
    SentencesResources
    Cells were subsequently stained with 1/1,000 dilution of an anti-rabbit AF488 antibody (Invitrogen catalogue number A11008).
    anti-rabbit
    suggested: (Molecular Probes Cat# A-11008, RRID:AB_143165)
    Experimental Models: Cell Lines
    SentencesResources
    Cell culture: VeroE6 cells (ATCC CRL-1586) were maintained in Gibco Dulbecco’s Modified Eagles Medium (DMEM) supplemented with 10 % (v/v
    VeroE6
    suggested: None
    Calu3 cells were imaged using the CellInsight quantitative fluorescence microscope (Thermo Fisher Scientific) at a magnification of 10 x, 49 fields/well, capturing the entire well.
    Calu3
    suggested: None
    Software and Algorithms
    SentencesResources
    http://www.bioinformatics.babraham.ac.uk/projects/fastqc/).
    http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
    suggested: (FastQC, RRID:SCR_014583)
    Bioinformatic analysis of RNA-seq reads: Quality and adapter trimming was performed using TrimGalore v0.6.4 (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) with default settings for automatic adapter detection.
    TrimGalore
    suggested: None
    http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/
    suggested: (Trim Galore, RRID:SCR_011847)
    Reads mapping to coding sequence (CDS) were counted to quantify transcripts capable of being translated into protein using featureCounts [68].
    featureCounts
    suggested: (featureCounts, RRID:SCR_012919)
    The Bioconductor package DESeq2 package in R (version 3.6.3) was used to test for differential expression between different experimental groups [69].
    Bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    Bioinformatic analysis of Ribo-seq reads: Adaptor was trimmed from the reads using Cutadapt followed by filtering for quality using FastX-toolkit as appropriate for small read analysis.
    FastX-toolkit
    suggested: (FASTX-Toolkit, RRID:SCR_005534)
    Trimmed and filtered reads were then mapped using Bowtie version 1.2.2 (http://bowtie-bio.sourceforge.net/manual.shtml) to the SARS-CoV-2 genome (accession number MT007544.1), human protein coding transcripts downloaded from BioMart (https://m.ensembl.org/info/data/biomart/index.html), miRNAs from miRbase (http://www.mirbase.org), rRNA from the silva database (https://www.arb-silva.de), snoRNA from the snoRNA-LBME-db database and tRNA from the GtRNAdb database [70].
    Bowtie
    suggested: (Bowtie, RRID:SCR_005476)
    BioMart
    suggested: (biomaRt, RRID:SCR_019214)
    miRbase
    suggested: (miRBase, RRID:SCR_017497)
    http://www.mirbase.org
    suggested: (microRNA database (miRBase, RRID:SCR_003152)
    https://www.arb-silva.de
    suggested: (SILVA, RRID:SCR_006423)
    Percentage of reads with at least one reported alignment were collected from alignment outputs and plotted using ggplot2 in R.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Reads mapping to non-coding RNA were filtered out and mapped to the human genome (GENCODE v35 primary assembly of GRCh38.p13) using STAR aligner as per RNA-seq reads with the following parameters --outFilterMismatchNmax 2 --quantMode TranscriptomeSAM GeneCounts --outSAMattributes MD NH –outFilterMultimapNmax 1.
    STAR
    suggested: (STAR, RRID:SCR_015899)
    Sorted bam files were converted to bed files and read depth at each genome position with 1-based coordinates determined using Bedtools version 2.29.2 [72] using a library normalisation factor obtained from DESeq2 analysis of the different experimental groups.
    Bedtools
    suggested: (BEDTools, RRID:SCR_006646)
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    For metagene analysis of coverage relative to the start codon, the distance from the transcription start site for each gene was extracted from the GENCODE v35 primary assembly of GRCh38.p13 gtf file then used to summarise coverage for across genes using scripts in R.
    GENCODE
    suggested: (GENCODE, RRID:SCR_014966)
    Read-length distributions were obtained using Samtools stats on position sorted alignment files from reads mapped to human CDS and 3’UTR sequences downloaded from Biomart (https://m.ensembl.org), read-length distributions were summarised across all genes.
    Samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)

    Results from OddPub: Thank you for sharing your code and data.

    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.

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  3. Version published to 10.1101/2021.03.03.433675 on bioRxiv