Analysis of Infected Host Gene Expression Reveals Repurposed Drug Candidates and Time-Dependent Host Response Dynamics for COVID-19

  1. Jing Xing
  2. Rama Shankar
  3. Aleksandra Drelich
  4. Shreya Paithankar
  5. Evgenii Chekalin
  6. Thomas Dexheimer
  7. Mei-Sze Chua
  8. Surender Rajasekaran
  9. Chien-Te Kent Tseng
  10. Bin Chen

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Abstract

The repurposing of existing drugs offers the potential to expedite therapeutic discovery against the current COVID-19 pandemic caused by the SARS-CoV-2 virus. We have developed an integrative approach to predict repurposed drug candidates that can reverse SARS-CoV-2-induced gene expression in host cells, and evaluate their efficacy against SARS-CoV-2 infection in vitro . We found that 13 virus-induced gene expression signatures computed from various viral preclinical models could be reversed by compounds previously identified to be effective against SARS- or MERS-CoV, as well as drug candidates recently reported to be efficacious against SARS-CoV-2. Based on the ability of candidate drugs to reverse these 13 infection signatures, as well as other clinical criteria, we identified 10 novel candidates. The four drugs bortezomib, dactolisib, alvocidib, and methotrexate inhibited SARS-CoV-2 infection-induced cytopathic effect in Vero E6 cells at < 1 µM, but only methotrexate did not exhibit unfavorable cytotoxicity. Although further improvement of cytotoxicity prediction and bench testing is required, our computational approach has the potential to rapidly and rationally identify repurposed drug candidates against SARS-CoV-2. The analysis of signature genes induced by SARS-CoV-2 also revealed interesting time-dependent host response dynamics and critical pathways for therapeutic interventions (e.g. Rho GTPase activation and cytokine signaling suppression).

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  1. Version published to 10.1101/2020.04.07.030734v2 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.04.07.030734: (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
    We found one RNA-Seq dataset (SRA: SRX4939022) for HepaRG cells treated with multiple compounds including ritonavir under multiple concentrations (ranging from 9 nM to 300 µM).
    HepaRG
    suggested: None
    SARS-CoV-2 (US_WA-1 isolate), the 3rd passage in Vero E6 cells from the original CDC (Atlanta) material and sequence confirmed, was used throughout the study.
    Vero E6
    suggested: RRID:CVCL_XD71)
    Software and Algorithms
    SentencesResources
    Computation of infection signatures: We obtained a total of 430 samples for “SARS-CoV” or “MERS-CoV” related data from ArrayExpress, Gene Expression Omnibus (GEO), and Sequence Read Archive (SRA).
    ArrayExpress
    suggested: (ArrayExpress, RRID:SCR_002964)
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    Sequence Read Archive
    suggested: (DDBJ Sequence Read Archive, RRID:SCR_001370)
    The probe values were collapsed based on Entrez Gene ID.
    Entrez Gene
    suggested: (Entrez Gene, RRID:SCR_002473)
    EdgeR was used to compute DE genes using the same criteria as used for microarray data.
    EdgeR
    suggested: (edgeR, RRID:SCR_012802)
    Gene ontology enrichment analysis of DE genes for each comparison was performed using the clusterprofiler R package.
    clusterprofiler
    suggested: (clusterProfiler, RRID:SCR_016884)
    Briefly, a full matrix comprising 476,251 signatures and 22,268 genes including 978 landmark genes (as of September 2013) was downloaded from the LINCS website (https://clue.io).
    https://clue.io
    suggested: (CMap, RRID:SCR_016204)
    The metainformation of the signatures (for example, cell line, treatment duration, treatment concentration) was retrieved via LINCS Application Program Interfaces.
    LINCS Application Program
    suggested: None
    Since the EnrichR dataset did not include any MERS-CoV signatures, we manually added the signatures of two MERS-CoV datasets (GSE79218, GSE79172, 8 in total) computed from the comparisons of separate infected groups and mock groups.
    EnrichR
    suggested: (Enrichr, RRID:SCR_001575)
    Software tools and statistical methods: All analyses were conducted in R (v3.5.1) or Python (v3.7) programming language.
    Python
    suggested: (IPython, RRID:SCR_001658)
    The ggplot2, pheatmap, and seaborn packages were used for data visualization.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study is restricted by the limitations of the LINCS datasets (e.g., limited coverage of the transcriptome). Another limitation is the use of drug-induced gene expression profiles derived from cancer cells that are different from the virus-infected cells used to generate the infection signatures. This may partially explain why antiviral drugs were not predicted as hits. Nevertheless, given the currently limited resources and the imperative need to find treatments for COVID-19, existing FDA-approved drugs that are identified, regardless of established MoA, should be further studied in infectious disease models. In conclusion, open science initiatives have allowed us to leverage public resources to rationally predict drug candidates that might reverse coronavirus-induced transcriptomic changes. The unbiased search for drug candidates based on reversal of gene expression could offer an effective and rapid means to propose drug candidates for further experimental testing, even those that may have unexpected MoA. However, more layers of information such as toxicity, experimental validation conditions, and clinical applicability could be incorporated to find improved therapeutics. The predicted drug list and valid infection signatures resulting from our study may provide a starting point for researchers to further validate these and other candidates during this time of urgency.

    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

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  3. Version published to 10.1101/2020.04.07.030734v1 on bioRxiv