A novel, anatomy-similar in vitro model of 3D airway epithelial for anti-coronavirus drug discovery

  1. Yaling Zhang
  2. Dingailu Ma
  3. David Jitao Zhang
  4. Xin’an Liu
  5. Qihui Zhu
  6. Li Wang
  7. Lu Gao

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Abstract

SARS-CoV-2 and its induced COVID-19 remains as a global health calamity. Severe symptoms and high mortality, caused by cytokine storm and acute respiratory distress syndrome in the lower respiratory airway, are always associated with elderly individuals and those with comorbidities; whereas mild or moderate COVID-19 patients have limited upper respiratory flu-like symptoms. There is an urgent need to investigate SARS-CoV-2 and other coronaviruses replication and immune responses in human respiratory systems. The human reconstituted airway epithelial air-liquid interface (ALI) models are the most physiologically relevant model for the investigation of coronavirus infection and virus-triggered innate immune signatures. We established ALI models representing both the upper and the lower respiratory airway to characterize the coronavirus infection kinetics, tissue pathophysiology, and innate immune signatures from upper and lower respiratory tract perspective. Our data suggested these in vitro ALI models maintain high physiological relevance with human airway tissues. The coronavirus induced immune response observed in these upper and lower respiratory airway models are similar to what has been reported in COVID-19 patients. The antiviral efficacy results of a few promising anti-coronavirus drugs in these models were consistent with previous reports and could be valuable for the human dose prediction. Taken together, our study demonstrates the importance of 3D airway epithelial ALI model for the understanding of coronavirus pathogenesis and the discovery and development of anti-coronavirus drugs.

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    SciScore for 10.1101/2021.03.03.433824: (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

    Software and Algorithms
    SentencesResources
    The sequences were processed and analyzed by GENEWIZ.
    GENEWIZ
    suggested: (GENEWIZ, RRID:SCR_003177)
    RNAseq analysis: Paired-end RNASeq reads were mapped to the human genome (hg38) with STAR aligner version 2.5.2a using default mapping parameters (Dobin et al., 2013).
    STAR
    suggested: (STAR, RRID:SCR_015899)
    The unmapped reads were later aligned to HCoV-229E genome using bowtie2 (v2.3.5) in sensitive mode (Langmead and Salzberg, 2012).
    bowtie2
    suggested: (Bowtie 2, RRID:SCR_016368)
    Numbers of mapped reads for all RefSeq transcript variants of a gene (counts) were combined into a single value by featureCounts software (Dobin et al., 2013; Liao et al., 2014) and normalized as tpm (transcripts per million).
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    featureCounts
    suggested: (featureCounts, RRID:SCR_012919)
    Differentially expressed genes were identified with edgeR (v3.32.1) at a fold-change >2 and aveExpr >2.(Robinson et al., 2010) Gene set enrichment analysis: Gene Set Enrichment Analysis (GSEA) was performed using R package fgsea (v1.16.0) (Korotkevich et al., 2021).
    edgeR
    suggested: (edgeR, RRID:SCR_012802)
    Normalized enrichment score (NES) was calculated with the internalized Molecular Signatures Database (MSigDB), MetaBase, and ImmuneSpace signatures.
    MetaBase
    suggested: (MetaBase, RRID:SCR_001762)
    BioQC analysis: Quality control based on the enrichment of lung tissue gene signatures was performed with BioQC (v1.19.3) (Zhang et al., 2017).
    BioQC
    suggested: None
    For statistical comparison one-way or two-way analysis of variance were performed with multiple comparison tests using GraphPad Prims software (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).
    GraphPad Prims
    suggested: None

    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 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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