Reconstructed signaling and regulatory networks identify potential drugs for SARS-CoV-2 infection

  1. Jun Ding
  2. Jose Lugo-Martinez
  3. Ye Yuan
  4. Jessie Huang
  5. Adam J. Hume
  6. Ellen L. Suder
  7. Elke Mühlberger
  8. Darrell N. Kotton
  9. Ziv Bar-Joseph

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Abstract

Several molecular datasets have been recently compiled to characterize the activity of SARS-CoV-2 within human cells. Here we extend computational methods to integrate several different types of sequence, functional and interaction data to reconstruct networks and pathways activated by the virus in host cells. We identify key proteins in these networks and further intersect them with genes differentially expressed at conditions that are known to impact viral activity. Several of the top ranked genes do not directly interact with virus proteins. We experimentally tested treatments for a number of the predicted targets. We show that blocking one of the predicted indirect targets significantly reduces viral loads in stem cell-derived alveolar epithelial type II cells (iAT2s).

Software and interactive visualization

https://github.com/phoenixding/sdremsc

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  1. Version published to 10.1101/2020.06.01.127589v3 on bioRxiv
  2. Version published to 10.1101/2020.06.01.127589v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.06.01.127589: (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

    Experimental Models: Cell Lines
    SentencesResources
    In addition to the bulk data we also used a single-cell RNA-seq data on Calu-3 cell line profiled at 0 (mock), 4, 8, 12 hours post SARS-CoV-2 infection.
    Calu-3
    suggested: KCLB Cat# 30055, RRID:CVCL_0609)
    Software and Algorithms
    SentencesResources
    Protein-protein interactions (PPIs) were obtained from the HIPPIE database [32], which contains more than 270000 annotated PPIs and for each provides a confidence score which was further used in our network analysis (see below).
    HIPPIE
    suggested: (HIPPIE, RRID:SCR_014651)
    Potential treatments for top genes: In a similar fashion to Gordon et al. [3], we searched public resources (ChEMBL25 [44], IUPHAR/BPS, Pharos [45
    Pharos
    suggested: (PHAROS, RRID:SCR_016258)
    5] and ZINC [46]), as well as literature in order to identify existing drugs and reagents that directly modulate the candidate genes derived from our network reconstruction and conditionspecific analyses.
    ZINC
    suggested: (Zinc, RRID:SCR_008596)
    Software and data availability: The single-cell extension of the SDREM model (named scSDREM) is implemented in Python.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    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.

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  4. Version published to 10.1101/2020.06.01.127589v1 on bioRxiv