Increased mTOR signaling, impaired autophagic flux and cell-to-cell viral transmission are hallmarks of SARS-CoV-2 infection

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Abstract

The COVID-19 disease caued by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has two characteristics that distinguish it from other viral infections. It affects more severely people with pre-existing comorbidities and viral load peaks prior to the onset of the symptoms. Investigating factors that could contribute to these characteristics, we found increased mTOR signaling and suppressed genes related to autophagy, lysosome, and vesicle fusion in Vero E6 cells infected with SARS-CoV-2. Transcriptomic data mining of bronchoalveolar epithelial cells from severe COVID-19 patients revealed that COVID-19 severity is associated with increased expression of genes related to mTOR signaling and decreased expression of genes related to autophagy, lysosome function, and vesicle fusion. SARS-CoV-2 infection in Vero E6 cells also resulted in virus retention inside the cells and trafficking of virus-bearing vesicles between neighboring cells. Our findings support a scenario where SARS-CoV-2 benefits from compromised autophagic flux and inhibited exocytosis in individuals with chronic hyperactivation of mTOR signaling, which might relate to undetectable proliferation and evasion of the immune system.

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  1. SciScore for 10.1101/2021.10.13.464225: (What is this?)

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

    Table 1: Rigor

    EthicsEuthanasia Agents: The plates were incubated at 37 °C, 5 % of CO2 atmosphere for 48 h.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Cells were then incubated overnight at 4 °C with primary antibodies at 1:100 dilution in PBST and 1 % BSA [SARS-COV-2 Spike S1 antibody (#HC2001 GenScript - #A02038), p62 antibody (#BD 610832), Lamp1 antibody (#BD 555798) and CD63 antibody (#BD 556019), according to the desired double IF.
    p62
    suggested: (BD Biosciences Cat# 610832, RRID:AB_398151)
    Lamp1
    suggested: (BD Biosciences Cat# 555798, RRID:AB_396132)
    CD63
    suggested: (BD Biosciences Cat# 556019, RRID:AB_396297)
    The slides were washed and incubated for 2 h with secondary antibodies (Alexa 488 anti-Human IgG Thermo Fisher - #A11013 and Alexa Fluor 555 Anti-Mouse IgG #A21422), diluted 1:500 in PBST+ 1 % BSA.
    anti-Human IgG
    suggested: (Molecular Probes Cat# A-11013, RRID:AB_141360)
    Anti-Mouse IgG
    suggested: (Molecular Probes Cat# A-21422, RRID:AB_141822)
    After blocking, the membranes were incubated with anti-mTOR (Cell Signaling, #2972), anti p-mTOR (Cell Signaling, #2971), p-S6K1 (Cell Signaling, #9234), anti-S6K1 (Cell Signaling, #2708), anti-pS6 (Cell Signaling, #2215), anti-S6 (Cell Signaling, #2317), anti-pULK1 (Cell Signaling, #5869), anti-ATG7 (Cell Signaling, #8558), anti-LC3 (Cell Signaling, #4108), anti-p62 (Cell Signaling, #88588), and anti-β-actin (Cell Signaling, #4967), antibodies overnight at 4 °C.
    anti-mTOR (Cell Signaling,
    suggested: (Cell Signaling Technology Cat# 2972, RRID:AB_330978)
    anti-mTOR (Cell Signaling, #2972), anti p-mTOR (Cell Signaling, #2971), p-S6K1 (Cell Signaling, #9234), anti-S6K1 (Cell Signaling, #2708)
    suggested: None
    anti p-mTOR (Cell Signaling, #2971), p-S6K1 (Cell Signaling, #9234), anti-S6K1 (Cell Signaling,
    suggested: None
    anti-S6
    suggested: (Cell Signaling Technology Cat# 2317, RRID:AB_2238583)
    anti-pULK1 (Cell Signaling,
    suggested: None
    anti-ATG7
    suggested: (Cell Signaling Technology Cat# 8558, RRID:AB_10831194)
    anti-LC3 (Cell Signaling, #4108), anti-p62 (Cell Signaling, #88588),
    suggested: None
    anti-β-actin (Cell Signaling,
    suggested: (Cell Signaling Technology Cat# 4967, RRID:AB_330288)
    Membranes were washed with TBS-T (3 times for 10 min) and incubated with horseradish peroxidase-conjugated secondary antibodies anti-mouse (Millipore, #AP308P), or anti-rabbit (Thermo Scientific, #31460) according to the primary antibody for 1 h at room temperature with constant agitation.
    horseradish peroxidase-conjugated secondary antibodies anti-mouse (Millipore,
    suggested: None
    anti-rabbit
    suggested: (Thermo Fisher Scientific Cat# 31460, RRID:AB_228341)
    Experimental Models: Cell Lines
    SentencesResources
    Vero E6: Vero E6 (African green monkey, Cercopithicus aethiops, kidney) cells were cultured in Dulbecco’s modified Eagle medium (DMEM; Sigma-Aldrich, USA) supplemented with 10% heat-inactivated fetal bovine serum (FBS; Sigma-Aldrich, USA) and 1% Penicillin-Streptomycin 100 U/mL and 100 μg/mL (Sigma-Aldrich, USA), and incubated in 5 % carbon dioxide atmosphere at 37 °C.
    Vero E6
    suggested: RRID:CVCL_XD71)
    To access the production of viable viral particles inside Vero cells in the experiments, the supernatants were removed from the wells, cells were washed once with PBS 1X, and the monolayer was collected and frozen with liquid nitrogen, followed by three series of freeze-and-thaw in liquid nitrogen and 37 °C water bath.
    Vero
    suggested: None
    Software and Algorithms
    SentencesResources
    Before image analysis, raw.czi files were automatically processed into deconvoluted Airyscan images using Zen Black 2.3 software.
    Zen
    suggested: None
    Gene level quantification was performed using Salmon [25] with the Chlorocebus sabaeus reference transcriptome obtained from NCBI as the index.
    Salmon
    suggested: (Salmon, RRID:SCR_017036)
    The R package tximport [26] was used to load quant.sf files to R and to create a DESeq2 object for differential expression analysis.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Geneset enrichment analysis (GSEA) of differentially expressed genes was performed using the fgsea R package [28] against a custom geneset containing all pathways and terms from Reactome, Gene Ontology (GO) biological process, GO cellular compartment, GO molecular function, Biocarta, KEGG, Hallmark pathways, and Wikipathways.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Wikipathways
    suggested: (WikiPathways, RRID:SCR_002134)
    Figures were elaborated using the ggplot2 and pheatmap R packages.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)
    Band densitometry was measured using ImageJ software.
    ImageJ
    suggested: (ImageJ, RRID:SCR_003070)
    Quantification and Statistical Analysis: The software packages GraphPad Prism 8.0 and 9.0 were used for statistical analyses of Western blotting and PFU, respectively.
    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.


    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.