Genetic determinants of COVID-19 drug efficacy revealed by genome-wide CRISPR screens

  1. Wei Jiang
  2. Ailing Yang
  3. Jingchuan Ma
  4. Dawei Lv
  5. Mingxian Liu
  6. Liang Xu
  7. Chao Wang
  8. Zhengjin He
  9. Shuo Chen
  10. Jie Zhao
  11. Shishuang Chen
  12. Qi Jiang
  13. Yankai Chu
  14. Lin Shan
  15. Zhaocai Zhou
  16. Yun Zhao
  17. Gang Long
  18. Hai Jiang

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Abstract

Immunomodulatory agents dexamethasone and colchicine, antiviral drugs remdesivir, favipiravir and ribavirin, as well as antimalarial drugs chloroquine phosphate and hydroxychloroquine are currently used in the combat against COVID-19 1–16 . However, whether some of these drugs have clinical efficacy for COVID-19 is under debate. Moreover, these drugs are applied in COVID-19 patients with little knowledge of genetic biomarkers, which will hurt patient outcome. To answer these questions, we designed a screen approach that could employ genome-wide sgRNA libraries to systematically uncover genes crucial for these drugs’ action. Here we present our findings, including genes crucial for the import, export, metabolic activation and inactivation of remdesivir, as well as genes that regulate colchicine and dexamethasone’s immunosuppressive effects. Our findings provide preliminary information for developing urgently needed genetic biomarkers for these drugs. Such biomarkers will help better interpret COVID-19 clinical trial data and point to how to stratify COVID-19 patients for proper treatment with these drugs.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.26.356279: (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

    Experimental Models: Cell Lines
    SentencesResources
    Phoenix, HeLa, MDA-MB-231, A549, 293T and Huh7 were cultured in Dulbecco’s modified Eagle’s medium supplemented with glutamate and 10% (v/v) FBS.
    HeLa
    suggested: None
    MDA-MB-231
    suggested: None
    A549
    suggested: None
    293T
    suggested: None
    Briefly, in vitro transcribed viral RNA was electroporated into Vero E6 cells, 72h-120h supernatants was harvested and titrated by plaque forming assay after removal of dead cells and cell debris.
    Vero E6
    suggested: None
    Infection of Huh7 derived cell line: Cells were seeded in 48-well plates for 24 hours before incubation with Zika virus at multiplicity of infection (MOI) equal to 1.
    Huh7
    suggested: None
    Software and Algorithms
    SentencesResources
    Next, a mouse KO sgRNA pooled library (Addgene: #1000000096, 10sgRNA per gene) was amplified at 1000X fold coverage, and the sgRNAs were digested with NheI/XhoI from the library.
    Addgene
    suggested: (Addgene, RRID:SCR_002037)

    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: We detected the following sentences addressing limitations in the study:
    There are several limitations to our study. As a loss of function screen, our study cannot identify certain kinds of genes. For example, if several proteins function redundantly in a drug-metabolizing step, our screen will not be able to uncover such proteins either in the enriched or depleted sgRNA dataset. Genome-wide overexpression screens may help complement this study. Moreover, there are many discussions that drug efficacy in COVID-19 patients may be significantly influenced by the timing of treatment. Our screen cannot address that question either. Due to the urgency of the situation, we also did not have time to further delve into the mechanisms of some of the screen hits. We present our screen results in the current form to facilitate relevant research, in the hope that among the list of screen hits, drug biomarkers will be established, such that better understanding of COVID-19 treatment will be achieved to help combat the pandemic.

    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.
    • 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.

  2. Version published to 10.1101/2020.10.26.356279v1 on bioRxiv