Modulating the transcriptional landscape of SARS-CoV-2 as an effective method for developing antiviral compounds

  1. Daisy A. Hoagland
  2. Daniel J.B. Clarke
  3. Rasmus Møller
  4. Yuling Han
  5. Liuliu Yang
  6. Megan L. Wojciechowicz
  7. Alexander Lachmann
  8. Kasopefoluwa Y. Oguntuyo
  9. Christian Stevens
  10. Benhur Lee
  11. Shuibing Chen
  12. Avi Ma’ayan
  13. Benjamin R tenOever

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Abstract

To interfere with the biology of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, we focused on restoring the transcriptional response induced by infection. Utilizing expression patterns of SARS-CoV-2-infected cells, we identified a region in gene expression space that was unique to virus infection and inversely proportional to the transcriptional footprint of known compounds characterized in the Library of Integrated Network-based Cellular Signatures. Here we demonstrate the successful identification of compounds that display efficacy in blocking SARS-CoV-2 replication based on their ability to counteract the virus-induced transcriptional landscape. These compounds were found to potently reduce viral load despite having no impact on viral entry or modulation of the host antiviral response in the absence of virus. RNA-Seq profiling implicated the induction of the cholesterol biosynthesis pathway as the underlying mechanism of inhibition and suggested that targeting this aspect of host biology may significantly reduce SARS-CoV-2 viral load.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.12.199687: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    protein (a kind gift by Dr. T. Moran, Center for Therapeutic Antibody Discovery at the Icahn School of Medicine at Mount Sinai) and rabbit monoclonal anti-vinculin (Abcam, ab129002).
    anti-vinculin ( Abcam ,
    suggested: None
    Primary antibodies were detected by IRDye 680 Goat anti-Mouse IgG secondary antibody (926-68070) and IRDye 800 Goat anti-Rabbit secondary antibody (926-32211) (Li-Cor) and visualized using a Li-Cor Odyssey CLx imaging system (Li-Cor).
    anti-Mouse IgG
    suggested: (LI-COR Biosciences Cat# 926-68070, RRID:AB_10956588)
    anti-Rabbit
    suggested: (LI-COR Biosciences Cat# 926-32211, RRID:AB_621843)
    Experimental Models: Cell Lines
    SentencesResources
    Cell Culture: Human adenocarcinomic alveolar basal epithelial (A549) cells (ATCC, CCL-185), African green monkey kidney epithelial Vero-E6 cells (ATCC, CRL-1586) were maintained at 37°C and 5% CO2 in Dulbecco’s Modified Eagle Medium (DMEM, Gibco) supplemented with 10% Fetal Bovine Serum (FBS, Corning).
    Vero-E6
    suggested: None
    A549ACE2 heterogeneous cell population was generated by transducing A549 cells with lentivirus without selection.
    A549ACE2
    suggested: None
    Infectious titers of SARS-CoV-2 were determined by plaque assay in Vero E6 cells in Minimum Essential Media supplemented with 4 mM L-glutamine, 0.2% BSA, 10 mM HEPES and 0.12% NaHCO3 and 0.7% agar.
    Vero E6
    suggested: RRID:CVCL_XD71)
    The RNA-seq signatures from the A549-ACE2 cells were computed by differential expression analysis with limma on the filtered count matrix.
    A549-ACE2
    suggested: None
    To titer these pseudoviruses, 20,000 Vero-CCL81 cells were seeded in a 96 well plate 20-24hrs prior to infection.
    Vero-CCL81
    suggested: None
    Supplementary Tables 1-6 DEGs from A549 cells treated with drug candidates Supplementary Tables 7-13 DEGs from A549 cells treated with drug candidates and infected with SARS-CoV-2 Supplementary Tables 14-20 DEGs from pancreatic organoids treated with drug candidates and infected with SARS-CoV-2 Supplementary Tables 21-22 BioJupies and Enrichr profiles for each drug condition
    A549
    suggested: None
    Software and Algorithms
    SentencesResources
    Differential expression analysis was performed with the Characteristic Direction method on the normalized matrix or with limma on the original count matrix filtered using the method described in (Chen et al., 2016).
    limma
    suggested: (LIMMA, RRID:SCR_010943)
    All differentially expressed genes were submitted for analysis with L1000FWD and Enrichr.
    Enrichr
    suggested: (Enrichr, RRID:SCR_001575)
    Supplementary Tables 1-6 DEGs from A549 cells treated with drug candidates Supplementary Tables 7-13 DEGs from A549 cells treated with drug candidates and infected with SARS-CoV-2 Supplementary Tables 14-20 DEGs from pancreatic organoids treated with drug candidates and infected with SARS-CoV-2 Supplementary Tables 21-22 BioJupies and Enrichr profiles for each drug condition
    BioJupies
    suggested: (BioJupies, RRID:SCR_016346)

    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.
    • No funding statement was detected.
    • 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.

  2. Version published to 10.1101/2020.07.12.199687 on bioRxiv