Effect of SARS-CoV-2 proteins on vascular permeability

  1. Rossana Rauti
  2. Meishar Shahoha
  3. Yael Leichtmann-Bardoogo
  4. Rami Nasser
  5. Eyal Paz
  6. Rina Tamir
  7. Victoria Miller
  8. Tal Babich
  9. Kfir Shaked
  10. Avner Ehrlich
  11. Konstantinos Ioannidis
  12. Yaakov Nahmias
  13. Roded Sharan
  14. Uri Ashery
  15. Ben Meir Maoz

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Abstract

Severe acute respiratory syndrome (SARS)-CoV-2 infection leads to severe disease associated with cytokine storm, vascular dysfunction, coagulation, and progressive lung damage. It affects several vital organs, seemingly through a pathological effect on endothelial cells. The SARS-CoV-2 genome encodes 29 proteins, whose contribution to the disease manifestations, and especially endothelial complications, is unknown. We cloned and expressed 26 of these proteins in human cells and characterized the endothelial response to overexpression of each, individually. Whereas most proteins induced significant changes in endothelial permeability, nsp2, nsp5_c145a (catalytic dead mutant of nsp5), and nsp7 also reduced CD31, and increased von Willebrand factor expression and IL-6, suggesting endothelial dysfunction. Using propagation-based analysis of a protein–protein interaction (PPI) network, we predicted the endothelial proteins affected by the viral proteins that potentially mediate these effects. We further applied our PPI model to identify the role of each SARS-CoV-2 protein in other tissues affected by coronavirus disease (COVID-19). While validating the PPI network model, we found that the tight junction (TJ) proteins cadherin-5, ZO-1, and β-catenin are affected by nsp2, nsp5_c145a, and nsp7 consistent with the model prediction. Overall, this work identifies the SARS-CoV-2 proteins that might be most detrimental in terms of endothelial dysfunction, thereby shedding light on vascular aspects of COVID-19.

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  1. Version published to 10.7554/elife.69314 on eLife
  2. ScreenIT

    SciScore for 10.1101/2021.02.27.433186: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationTo assess the statistical significance of the obtained scores, we compared them to those computed on 100 randomized networks that preserve node degrees.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    The following primary antibodies were applied overnight in PBS at 4 °C: rabbit anti-VWF (Abcam, Cambridge, UK) and rabbit anti-CD31 (Abcam) against platelet endothelial cell adhesion molecule 1 (PECAM1).
    anti-VWF
    suggested: None
    anti-CD31 ( Abcam ) against platelet endothelial cell adhesion molecule 1
    suggested: None
    PECAM1
    suggested: None
    Cells were then washed three times in PBS and stained with the secondary antibody, anti-rabbit Alexa Fluor 488 (Invitrogen, Carlsbad, CA), for 1 h at RT.
    anti-rabbit
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    HEK293T cells (ATCC, Manassas, VA) were seeded in 10-cm cell-culture plates at a density of 4 × 106 cells/plate.
    HEK293T
    suggested: None
    Then, the HUVEC, harvested using a DetachKit (PromoCell), were seeded inside the culture platforms at a density of 250,000 cells/cm2 and grown for 3 days.
    HUVEC
    suggested: None
    Software and Algorithms
    SentencesResources
    Image reconstruction and analysis were done using open-source ImageJ software52.
    ImageJ
    suggested: (ImageJ, RRID:SCR_003070)
    Statistically significant differences among multiple groups were evaluated by F-statistic with two-way ANOVA, followed by the Holm–Sidak test for multiple comparisons (GraphPad Prism 8.4.3).
    GraphPad
    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: We detected the following sentences addressing limitations in the study:
    Finally, we would like to point out some of the limitations of our study. The two major limitations of our approach are: (a) inability to identify the effect of multiple proteins; (b) neglecting the effect of the coronavirus structure and binding on the cellular response. The former point can be overcome by combining different SARS-CoV-2 proteins in a well. However, since the SARS-CoV-2 expresses 29 proteins, there are 29! combinations, which is about ~9 × 1030. Therefore, we decided to focus on individual proteins, and allow further studies to pursue any combinations of interest. Regarding the latter limitation, we did not include the coronavirus structure (including the ACE2 receptors) in this study, because many studies have already demonstrated the cellular response to this structure19,49,50, and how tissues that do not have significant ACE2 expression (such as neurons, immune components such as B and T lymphocytes, and macrophages) are affected by the virus remains an open question.

    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 10.1101/2021.02.27.433186 on bioRxiv