SARS-CoV-2 infection and replication in human gastric organoids

  1. Giovanni Giuseppe Giobbe
  2. Francesco Bonfante
  3. Brendan C. Jones
  4. Onelia Gagliano
  5. Camilla Luni
  6. Elisa Zambaiti
  7. Silvia Perin
  8. Cecilia Laterza
  9. Georg Busslinger
  10. Hannah Stuart
  11. Matteo Pagliari
  12. Alessio Bortolami
  13. Eva Mazzetto
  14. Anna Manfredi
  15. Chiara Colantuono
  16. Lucio Di Filippo
  17. Alessandro Filippo Pellegata
  18. Valentina Panzarin
  19. Nikhil Thapar
  20. Vivian Sze Wing Li
  21. Simon Eaton
  22. Davide Cacchiarelli
  23. Hans Clevers
  24. Nicola Elvassore
  25. Paolo De Coppi

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Abstract

COVID-19 typically manifests as a respiratory illness, but several clinical reports have described gastrointestinal symptoms. This is particularly true in children in whom gastrointestinal symptoms are frequent and viral shedding outlasts viral clearance from the respiratory system. These observations raise the question of whether the virus can replicate within the stomach. Here we generate gastric organoids from fetal, pediatric, and adult biopsies as in vitro models of SARS-CoV-2 infection. To facilitate infection, we induce reverse polarity in the gastric organoids. We find that the pediatric and late fetal gastric organoids are susceptible to infection with SARS-CoV-2, while viral replication is significantly lower in undifferentiated organoids of early fetal and adult origin. We demonstrate that adult gastric organoids are more susceptible to infection following differentiation. We perform transcriptomic analysis to reveal a moderate innate antiviral response and a lack of differentially expressed genes belonging to the interferon family. Collectively, we show that the virus can efficiently infect the gastric epithelium, suggesting that the stomach might have an active role in fecal-oral SARS-CoV-2 transmission.

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  1. Version published to 10.1038/s41467-021-26762-2
  2. ScreenIT

    SciScore for 10.1101/2020.06.24.167049: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Fetal samples were sourced via the Joint MRC/Wellcome Trust Human Developmental Biology Resource under informed ethical consent with Research Tissue Bank ethical approval (08/H0712/34+5).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Immunostaining of infected cells was performed by incubation of the J2 anti-dsRNA monoclonal antibody (1:10,000; Scicons) for 1 hour, followed by 1-hour incubation with peroxidase-labeled goat anti-mouse antibodies (1:1000; DAKO) and a 7 min incubation with the True Blue™ (KPL) peroxidase substrate.
    J2
    suggested: (US Biological Cat# U1000-87M, RRID:AB_2210756)
    anti-dsRNA
    suggested: (Millipore Cat# MABE1134, RRID:AB_2819101)
    anti-mouse
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Briefly, the swab viral transport medium was filtered through a 0.22 µm filter, serially diluted and incubated onto a confluent layer of Vero E6 cells, for 5 days.
    Vero E6
    suggested: RRID:CVCL_XD71)
    Software and Algorithms
    SentencesResources
    Results were analyzed through GenomeStudio software (Illumina).
    GenomeStudio
    suggested: (GenomeStudio, RRID:SCR_010973)
    GAPDH expression was used to normalize Ct values for gene expression, and data were shown as relative fold change to controls (early fetal stage), using ΔΔCt method, and presented as Mean ± SEM. RNA Seq and transcriptome bioinformatic analyses: For RNA-seq data of original tissues and organoids with spontaneous polarity, total RNA (100 ng) from each sample was prepared using QuantSeq 3’
    QuantSeq
    suggested: None
    Alignment was performed with STAR 2.6.0a37 on hg38 reference assembly obtained from cellRanger website (Ensembl 93), following online site guide.
    STAR
    suggested: (STAR, RRID:SCR_015899)
    The expression levels of genes were determined with htseq-count 0.9.1 by using cellRanger pre-build genes annotations (Ensembl Assembly 93).
    Ensembl
    suggested: (Ensembl, RRID:SCR_002344)
    Illumina novaSeq base call (BCL) files were converted into fastq files through bcl2fastq (version v2.20.0.422) following software guide.
    bcl2fastq
    suggested: (bcl2fastq , RRID:SCR_015058)
    Alignment was performed with STAR 2.6.0a34 on hg38 reference assembly obtained from the Gencode website (primary assembly v. 32).
    Gencode
    suggested: (GENCODE, RRID:SCR_014966)
    Transcripts estimated counts were determined with RSEM 1.3.038 by using the Gencode v.
    RSEM
    suggested: (RSEM, RRID:SCR_013027)
    Reactome hierarchy was visualized using ClueGO within Cytoscape41.
    ClueGO
    suggested: (ClueGO, RRID:SCR_005748)
    Log-normalized expression data were analyzed by the Quantitative Set Analysis for Gene Expression (QuSAGE)27 Bioconductor package.
    QuSAGE)27
    suggested: None
    Bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    Statistical analysis: Statistical analyses were performed using the following software: MATLAB (v. R2017a) for PCA, pie plot, bar plot, hierarchical clustering with proteomic and RNA-seq data.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)
    GraphPad Prism Mac (v. 6.0h) was used with all other graphs and charts.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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 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: Please consider improving the rainbow (“jet”) colormap(s) used on pages 18, 19, 21, 22, 26 and 27. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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.

  3. Version published to 10.1101/2020.06.24.167049v1 on bioRxiv