SARS-CoV-2 diverges from other betacoronaviruses in only partially activating the IRE1α/XBP1 ER stress pathway in human lung-derived cells

  1. Long C. Nguyen
  2. David M. Renner
  3. Diane Silva
  4. Dongbo Yang
  5. Nicholas Parenti
  6. Kaeri M. Medina
  7. Vlad Nicolaescu
  8. Haley Gula
  9. Nir Drayman
  10. Andrea Valdespino
  11. Adil Mohamed
  12. Christopher Dann
  13. Kristin Wannemo
  14. Lydia Robinson-Mailman
  15. Alan Gonzalez
  16. Letícia Stock
  17. Mengrui Cao
  18. Zeyu Qiao
  19. Raymond E. Moellering
  20. Savas Tay
  21. Glenn Randall
  22. Michael F. Beers
  23. Marsha Rich Rosner
  24. Scott A. Oakes
  25. Susan R. Weiss

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has killed over 6 million individuals worldwide and continues to spread in countries where vaccines are not yet widely available, or its citizens are hesitant to become vaccinated. Therefore, it is critical to unravel the molecular mechanisms that allow SARS-CoV-2 and other coronaviruses to infect and overtake the host machinery of human cells. Coronavirus replication triggers endoplasmic reticulum (ER) stress and activation of the unfolded protein response (UPR), a key host cell pathway widely believed essential for viral replication. We examined the master UPR sensor IRE1α kinase/RNase and its downstream transcription factor effector XBP1s, which is processed through an IRE1α-mediated mRNA splicing event, in human lung-derived cells infected with betacoronaviruses. We found human respiratory coronavirus OC43 (HCoV-OC43), Middle East respiratory syndrome coronavirus (MERS-CoV), and murine coronavirus (MHV) all induce ER stress and strongly trigger the kinase and RNase activities of IRE1α as well as XBP1 splicing. In contrast, SARS-CoV-2 only partially activates IRE1α through autophosphorylation, but its RNase activity fails to splice XBP1. Moreover, while IRE1α was dispensable for replication in human cells for all coronaviruses tested, it was required for maximal expression of genes associated with several key cellular functions, including the interferon signaling pathway, during SARS-CoV-2 infection. Our data suggest that SARS-CoV-2 actively inhibits the RNase of autophosphorylated IRE1α, perhaps as a strategy to eliminate detection by the host immune system.

IMPORTANCE

SARS-CoV-2 is the third lethal respiratory coronavirus after MERS-CoV and SARS-CoV to emerge this century, causing millions of deaths world-wide. Other common coronaviruses such as HCoV-OC43 cause less severe respiratory disease. Thus, it is imperative to understand the similarities and differences among these viruses in how each interacts with host cells. We focused here on the inositol-requiring enzyme 1α (IRE1α) pathway, part of the host unfolded protein response to virus-induced stress. We found that while MERS-CoV and HCoV-OC43 fully activate the IRE1α kinase and RNase activities, SARS-CoV-2 only partially activates IRE1α, promoting its kinase activity but not RNase activity. Based on IRE1α-dependent gene expression changes during infection, we propose that SARS-CoV-2 prevents IRE1α RNase activation as a strategy to limit detection by the host immune system.

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  1. Version published to 10.1101/2021.12.30.474519v2 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2021.12.30.474519: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    African green monkey kidney Vero cells (E6) (ATCC CRL-1586) and VeroCCL81 cells (ATCC CCL-81) were cultured in Dulbecco’s modified Eagle’s medium (DMEM; Gibco catalog no. 11965), supplemented with 10% fetal bovine serum (FBS), 100 U/ml of penicillin, 100 μg/ml streptomycin, 50 μg/ml gentamicin (Gibco catalog no. 15750)
    Vero
    suggested: ATCC Cat# CRL-1586, RRID:CVCL_0574)
    Human HEK 293T cells (ATCC) were cultured in DMEM supplemented with 10% FBS.
    HEK 293T
    suggested: None
    A549-ACE2 cells, used in Figure 3I&J, Figure 4, Figure 6, and Figure S3 were a kind gift of Benjamin TenOever, Mt Sinai Icahn School of Medicine.
    A549-ACE2
    suggested: None
    Viruses: SARS-CoV-2 (USA-WA1/2020 isolate) was obtained from BEI Resources, NIAID, NIH or provided by Natalia Thornburg, World Reference Center for Emerging Viruses and Arboviruses (Galveston, Texas), and propagated in VeroE6-TMPRSS2 cells.
    VeroE6-TMPRSS2
    suggested: None
    Recombinant MERS-CoV was described previously (1) and propagated in VeroCCL81 cells.
    VeroCCL81
    suggested: None
    OC43 was obtained from ATCC (VR-1558) grown and titrated on A549-mRuby cells as desrcibed (51) or VeroE6 cells at 33 degrees Celsius.
    A549-mRuby
    suggested: None
    VeroE6
    suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)
    MHV-A59 was obtained from Thomas Gallagher, Loyola’s Stritch School of Medicine (52) and propagated on A549-MHVR cells or on murine 17CL-1 cells.
    A549-MHVR
    suggested: None
    In brief, A549 cells were seeded at 3×105 cells per well in a 12-well plate for infections.
    A549
    suggested: None
    Calu-3 cells were seeded similarly onto rat tail collagen type I coated plates (Corning #356500).
    Calu-3
    suggested: None
    Plaque assays were performed using VeroE6 cells for SARS-CoV-2 and OC43; VeroCCL81 cells for MERS-CoV; and L2 cells for MHV.
    L2
    suggested: ATCC Cat# CCL-149, RRID:CVCL_0383)
    Software and Algorithms
    SentencesResources
    RNA sample quality check, library construction, and sequencing were performed by GeneWiz following standard protocols.
    GeneWiz
    suggested: (GENEWIZ, RRID:SCR_003177)
    Read quality was assessed using FastQC v0.11.2 as described by Andrews, S. (2010).
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    Raw sequencing reads from each sample were quality and adapter trimmed using BBDuk 38.73 as described by Bushnell, B at “BBTools software package.
    “BBTools
    suggested: (Bestus Bioinformaticus Tools, RRID:SCR_016968)
    ”: http://sourceforge.net/projects/bbmap 578 (2014): 579.
    http://sourceforge.net/projects/bbmap
    suggested: (BBmap, RRID:SCR_016965)
    The reads were mapped to the human genome (hg38 with Ensembl V98 annotation) using RNA STAR 2.7.1a(53).
    Ensembl
    suggested: (Ensembl, RRID:SCR_002344)
    STAR
    suggested: (STAR, RRID:SCR_004463)
    The resulting BAM files were counted by featureCounts 1.6.4 to count the number of reads for each gene(54).
    featureCounts
    suggested: (featureCounts, RRID:SCR_012919)
    A PCA plot of RNA-seq samples and a normalized gene expression matrix were also generated by DESeq2.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Specifically, Ingenuity Pathway Analysis (IPA) (https://www.qiagenbioinformatics.com/products/ingenuitypathway-analysis) was used to predict activities of related canonical pathways based on host gene expression changes following viral infection.
    Ingenuity Pathway Analysis
    suggested: (Ingenuity Pathway Analysis, RRID:SCR_008653)
    Activation z-scores for every virus and canonical pathway combination were plotted as a heatmap using Morpheus (https://software.broadinstitute.org/morpheus).
    Morpheus
    suggested: (Morpheus, RRID:SCR_014975)
    Statistical analysis: All statistical analyses and plotting of data were performed using GraphPad Prism software.
    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 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.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.12.30.474519v1 on bioRxiv