Characterization and treatment of SARS-CoV-2 in nasal and bronchial human airway epithelia

  1. Andrés Pizzorno
  2. Blandine Padey
  3. Thomas Julien
  4. Sophie Trouillet-Assant
  5. Aurélien Traversier
  6. Elisabeth Errazuriz-Cerda
  7. Julien Fouret
  8. Julia Dubois
  9. Alexandre Gaymard
  10. François-Xavier Lescure
  11. Victoria Dulière
  12. Pauline Brun
  13. Samuel Constant
  14. Julien Poissy
  15. Bruno Lina
  16. Yazdan Yazdanpanah
  17. Olivier Terrier
  18. Manuel Rosa-Calatrava

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Abstract

In the current COVID-19 pandemic context, proposing and validating effective treatments represents a major challenge. However, the lack of biologically relevant pre-clinical experimental models of SARS-CoV-2 infection as a complement of classic cell lines represents a major barrier for scientific and medical progress. Here, we advantageously used human reconstituted airway epithelial models of nasal or bronchial origin to characterize viral infection kinetics, tissue-level remodeling of the cellular ultrastructure and transcriptional immune signatures induced by SARS-CoV-2. Our results underline the relevance of this model for the preclinical evaluation of antiviral candidates. Foremost, we provide evidence on the antiviral efficacy of remdesivir and the therapeutic potential of the remdesivir-diltiazem combination as a rapidly available option to respond to the current unmet medical need imposed by COVID-19.

One Sentence Summary

New insights on SARS-CoV-2 biology and drug combination therapies against COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.31.017889: (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.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your 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 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 10.1101/2020.03.31.017889 on bioRxiv