Identification of SARS-CoV-2–induced pathways reveals drug repurposing strategies

  1. Namshik Han
  2. Woochang Hwang
  3. Konstantinos Tzelepis
  4. Patrick Schmerer
  5. Eliza Yankova
  6. Méabh MacMahon
  7. Winnie Lei
  8. Nicholas M. Katritsis
  9. Anika Liu
  10. Ulrike Felgenhauer
  11. Alison Schuldt
  12. Rebecca Harris
  13. Kathryn Chapman
  14. Frank McCaughan
  15. Friedemann Weber
  16. Tony Kouzarides

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Abstract

Data-driven computational analysis and simulation identified drug repurposing opportunities for COVID-19.

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  1. Version published to 10.1126/sciadv.abh3032
  2. ScreenIT

    SciScore for 10.1101/2020.08.24.265496: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    Antibodies: Western blot experiments were performed using the following antibodies: GAPDH (Abcam, ab9484), Phospho-MAPKAPK-2 (Thr334, Cell Signalling, 3007)
    GAPDH
    suggested: (Abcam Cat# ab9484, RRID:AB_307274)
    Phospho-MAPKAPK-2 ( Thr334 , Cell Signalling , 3007)
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    For infection experiments in Vero E6 and Calu-3 cells, SARS-CoV-2 (strain München-1.2/2020/984) at MOI=0.01 pfu/cell for 24 hours.
    Vero E6
    suggested: None
    Cytotoxicity cell viability assays: Cytotoxicity was performed in Vero E6 and Calu-3 cells using Neutral Red (Abcam, ab234039) and MTT assay (Roche) respectively, according to the manufacturer’s instructions.
    Calu-3
    suggested: None
    Software and Algorithms
    SentencesResources
    SIP network construction: SIP network was constructed of all shortest paths between DIP and DEP in a human protein-protein interaction network from STRING database(Szklarczyk et al., 2019).
    STRING
    suggested: (STRING, RRID:SCR_005223)
    All shortest paths between DIP and DEP were found using the python package NetworkX(Hagberg et al., 2008).
    python
    suggested: (IPython, RRID:SCR_001658)
    Networks were visualized using Gephi 0.9.2(Bastian et al., 2009) (Figure S1).
    Gephi
    suggested: (Gephi, RRID:SCR_004293)
    Subcellular localization information for key proteins was found using COMPARTMENT database(Binder et al., 2014).
    COMPARTMENT
    suggested: None
    The SOM Toolbox package(Vatanen et al., 2015) for Matlab was used for this analysis.
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)

    Results from OddPub: Thank you for sharing your code.

    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

    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 10.1101/2020.08.24.265496v1 on bioRxiv