A phenomics approach for antiviral drug discovery

  1. Jonne Rietdijk
  2. Marianna Tampere
  3. Aleksandra Pettke
  4. Polina Georgiev
  5. Maris Lapins
  6. Ulrika Warpman-Berglund
  7. Ola Spjuth
  8. Marjo-Riitta Puumalainen
  9. Jordi Carreras-Puigvert

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Abstract

Background

The emergence and continued global spread of the current COVID-19 pandemic has highlighted the need for methods to identify novel or repurposed therapeutic drugs in a fast and effective way. Despite the availability of methods for the discovery of antiviral drugs, the majority tend to focus on the effects of such drugs on a given virus, its constituent proteins, or enzymatic activity, often neglecting the consequences on host cells. This may lead to partial assessment of the efficacy of the tested anti-viral compounds, as potential toxicity impacting the overall physiology of host cells may mask the effects of both viral infection and drug candidates. Here we present a method able to assess the general health of host cells based on morphological profiling, for untargeted phenotypic drug screening against viral infections.

Results

We combine Cell Painting with antibody-based detection of viral infection in a single assay. We designed an image analysis pipeline for segmentation and classification of virus-infected and non-infected cells, followed by extraction of morphological properties. We show that this methodology can successfully capture virus-induced phenotypic signatures of MRC-5 human lung fibroblasts infected with human coronavirus 229E (CoV-229E). Moreover, we demonstrate that our method can be used in phenotypic drug screening using a panel of nine host- and virus-targeting antivirals. Treatment with effective antiviral compounds reversed the morphological profile of the host cells towards a non-infected state.

Conclusions

The phenomics approach presented here, which makes use of a modified Cell Painting protocol by incorporating an anti-virus antibody stain, can be used for the unbiased morphological profiling of virus infection on host cells. The method can identify antiviral reference compounds, as well as novel antivirals, demonstrating its suitability to be implemented as a strategy for antiviral drug repurposing and drug discovery.

Article activity feed

  1. Version published to 10.1186/s12915-021-01086-1
  2. Version published to 10.1101/2021.01.13.423947v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.01.13.423947: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationCompound and virus infection conditions were randomized within the plates and multiple plate layouts were used to compensate for systematic effects related to well position.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line AuthenticationContamination: The cells were maintained at 37 °C under 5% CO2 and the cell culture was routinely tested for Mycoplasma using a luminescence-based MycoAlert kit (Lonza)

    Table 2: Resources

    Antibodies
    SentencesResources
    Wheat Germ Agglutinin/Alexa Fluor 555 (Invitrogen, cat.no W32464) and Phalloidin/Alexa Fluor 568 (Invitrogen, cat.no A12380) was prepared in PBS in addition to Goat anti-Mouse IgG (H+L) Cross-Adsorbed Secondary Antibody, Alexa Fluor 647 (Invitrogen Catalog # A-21235).
    anti-Mouse IgG
    suggested: (Molecular Probes Cat# A-21235, RRID:AB_2535804)
    Experimental Models: Cell Lines
    SentencesResources
    Cell culture: MRC5 cells (from ATCC, Manassas, VA, USA) were cultured in Minimum Essential Media supplemented with 10% (v/v
    MRC5
    suggested: None
    Virus titers were determined in Huh7 cells by end point dilution assay combined with high-throughput immunofluorescence imaging of viral protein staining as described previously (16).
    Huh7
    suggested: CLS Cat# 300156/p7178_HuH7, RRID:CVCL_0336)
    Software and Algorithms
    SentencesResources
    Statistics: Statistical analysis was done using GraphPad Prism version 9.0 (GraphPad Software Inc).
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Downstream analysis of the feature data was performed using Python 3.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    While this could be considered a caveat of this method, assay and conditions optimisation are a requisite for any type of screening method. The antiviral activity of TH6744, TH3289 and TH5487 has previously been characterized on viral progeny release (16), which differs from the primary virus infection used in this work. This methodological difference might explain the mild antiviral activity found by both the traditional antibody-based approach and our phenomics workflow. However, the mild antiviral activity of these newly developed compounds, in particular of TH6744, was reflected by its morphological profile and PCA, which showed that TH6744 partially rescued the virus-induced phenotype. A clear advantage of using morphological profiling to screen for antiviral drugs is the in-depth analysis of the host cell status upon infection as well as compound treatment. Thus, despite not being able to recapitulate the antiviral activities of the above mentioned compounds, using our phenomics workflow, we have obtained important information that can be used to further understand the biological effect of both a given virus or a given compound, and importantly it validates our method to identify potential novel antiviral compounds. Overall, we demonstrated that our phenomics workflow, which includes an adapted Cell Painting protocol, complemented with a custom image analysis pipeline, can be utilized the unbiased study of virus-induced effects on host cells that can be leveraged for dr...

    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 10.1101/2021.01.13.423947v1 on bioRxiv