ILRUN downregulates ACE2 expression and blocks infection of human cells by SARS-CoV-2

  1. Leon Tribolet
  2. Marina R. Alexander
  3. Aaron M. Brice
  4. Petrus Jansen van Vuren
  5. Christina L. Rootes
  6. Kostlend Mara
  7. Meg McDonald
  8. Kerri L. Bruce
  9. Tamara J. Gough
  10. Shuning Shi
  11. Christopher Cowled
  12. Andrew G. D. Bean
  13. Cameron R. Stewart

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Abstract

The human protein-coding gene ILRUN (inflammation and lipid regulator with UBA-like and NBR1-like domain, previously C6orf106) is a recently-characterised inhibitor of the transcription regulators p300 and CREB-binding protein (CBP). Here we have utilised RNA-seq to define cellular pathways regulated by ILRUN in the context of severe acute respiratory syndrome-associated coronavirus-2 (SARS-CoV-2) infection. We find that inhibition of ILRUN expression increases cellular expression of several members of the renin-angiotensin aldosterone system (RAAS), including the SARS-CoV-2 entry receptor angiotensin converting enzyme 2 (ACE2). Furthermore, inhibition of ILRUN results in increased SARS-CoV-2 replication. These data identify ILRUN as a novel inhibitor of SARS-CoV-2 replication and represents, to our knowledge, the first report of ILRUN as a regulator of the RAAS.

SIGNIFICANCE STATEMENT

There is no doubt that the current rapid global spread of COVID-19 has had significant and far-reaching impacts on our health and economy and will continue to do so. Research in emerging infectious diseases, such as severe acute respiratory syndrome-associated coronavirus (SARS-CoV-2), is growing rapidly, with new breakthroughs in the understanding of host-virus interactions and the development of innovative and exciting therapeutic strategies and new knowledge and tools to better protect against the impacts of disease. The human protein-coding gene ILRUN is a recently-characterised inhibitor of the transcription regulators p300 and CREB-binding protein (CBP). Here we present the first evidence that ILRUN modulation has implications for SARS-CoV-2 infections. Virus infectivity assays confirmed that gene silencing of ILRUN had a proviral effect and increased SARS-CoV-2 replication, whilst over-expression of ILRUN inhibited SARS-CoV-2 production. Additionally, we observed that ILRUN also regulates the expression of key elements of the RAAS. These data have important implications for the development of antiviral strategies to deal with the current SARS-CoV-2 pandemic.

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  1. ScreenIT

    SciScore for 10.1101/2020.11.13.381343: (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
    Caco-2 cells were maintained in Gibco Modified Eagles Medium (MEM) supplemented with 20 % (v/v) FCS, 10 mM HEPES, 0.1 mM non-essential amino acids, 2 mM glutamine, 1 mM sodium pyruvate, 100 U/mL penicillin, and 100 μg/mL streptomycin (Life Technologies).
    Caco-2
    suggested: None
    The isolate of SARS-CoV-2 (BetaCoV/Australia/VIC01/2020) was received from the Victorian Infectious Disease Reference Laboratory (VIDRL, Melbourne, Australia) and passaged in VeroE6 cells for isolation, followed by passaging in VeroE6 cells for stock generation.
    VeroE6
    suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)
    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 data: 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)
    Trimmed reads were mapped to the National Cener for Biotechnology Information (NCBI) human reference genome (GRCh38) downloaded from Illumina iGenomes (http://igenomes.illumina.com.s3-website-us-east-1.amazonaws.com/Homo_sapiens/NCBI/GRCh38/Homo_sapiens_NCBI_GRCh38.ta r.gz) using Tophat v2.1.1 (49).
    Tophat
    suggested: (TopHat, RRID:SCR_013035)
    The number of reads overlapping each gene in the NCBI annotated reference genome (GRCh38) were counted using htseq-count v0.11.2 within Python v3.7.2 (50), using the intersection-nonempty mode to handle reads overlapping more than one feature.
    htseq-count
    suggested: (htseq-count, RRID:SCR_011867)
    The Bioconductor package DESeq2 was used to test for differential expression between different experimental groups (51).
    Bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    KEGG) pathway mapping (52), custom python scripts were used to prepare input lists for conversion of gene symbols to Entrez gene IDs using bioDBnet’s db2db tool (https://biodbnet-abcc.ncifcrf.gov/db/db2db.php) and generation of hex codes based on fold change for colour mapping of KEGG pathways (https://www.genome.jp/kegg/tool/map_pathway2.html).
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    python
    suggested: (IPython, RRID:SCR_001658)
    Trimmed reads were mapped to a SARS-CoV-2 reference genome of the virus used in experiments (MT007544; SARS-CoV-2/human/AUS/VIC01/2020) using bowtie v2.3.4 (53).
    bowtie
    suggested: (Bowtie, RRID:SCR_005476)
    Mapped viral reads were counted using samtools v1.10.0 (Li et al., 2009) and coverview v1.4.4 (54) then normalised to library read content.
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    Analysis of publicly available data: The NCBI Gene Expression Omnibus was searched for suitable RNA-seq datasets and GSE Accession number entered into the GREIN : GEO RNA-seq Experiments Interactive Navigator (http://www.ilincs.org/apps/grein/) to retrieve metadata and normalized counts.
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    Normalised counts were plotted for each gene using ggplot2 package in R.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Statistics: The difference between two groups was analysed in GraphPad Prism by a two-tailed Student’s t test and between multiple groups by one-way ANOVA.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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

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  2. Version published to 10.1101/2020.11.13.381343 on bioRxiv