Profiling of lung SARS-CoV-2 and influenza virus infection dissects virus-specific host responses and gene signatures

  1. Arutha Kulasinghe
  2. Chin Wee Tan
  3. Anna Flavia Ribeiro dos Santos Miggiolaro
  4. James Monkman
  5. Habib SadeghiRad
  6. Dharmesh D. Bhuva
  7. Jarbas da Silva Motta Junior
  8. Caroline Busatta Vaz de Paula
  9. Seigo Nagashima
  10. Cristina Pellegrino Baena
  11. Paulo Souza-Fonseca-Guimaraes
  12. Lucia de Noronha
  13. Timothy McCulloch
  14. Gustavo Rodrigues Rossi
  15. Caroline Cooper
  16. Benjamin Tang
  17. Kirsty R. Short
  18. Melissa J. Davis
  19. Fernando Souza-Fonseca-Guimaraes
  20. Gabrielle T. Belz
  21. Ken O'Byrne

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which emerged in late 2019 has spread globally, causing a pandemic of respiratory illness designated coronavirus disease 2019 (COVID-19). A better definition of the pulmonary host response to SARS-CoV-2 infection is required to understand viral pathogenesis and to validate putative COVID-19 biomarkers that have been proposed in clinical studies.

Methods

Here, we use targeted transcriptomics of formalin-fixed paraffin-embedded tissue using the NanoString GeoMX platform to generate an in-depth picture of the pulmonary transcriptional landscape of COVID-19, pandemic H1N1 influenza and uninfected control patients.

Results

Host transcriptomics showed a significant upregulation of genes associated with inflammation, type I interferon production, coagulation and angiogenesis in the lungs of COVID-19 patients compared to non-infected controls. SARS-CoV-2 was non-uniformly distributed in lungs (emphasising the advantages of spatial transcriptomics) with the areas of high viral load associated with an increased type I interferon response. Once the dominant cell type present in the sample, within patient correlations and patient–patient variation, had been controlled for, only a very limited number of genes were differentially expressed between the lungs of fatal influenza and COVID-19 patients. Strikingly, the interferon-associated gene IFI27 , previously identified as a useful blood biomarker to differentiate bacterial and viral lung infections, was significantly upregulated in the lungs of COVID-19 patients compared to patients with influenza.

Conclusion

Collectively, these data demonstrate that spatial transcriptomics is a powerful tool to identify novel gene signatures within tissues, offering new insights into the pathogenesis of SARS-COV-2 to aid in patient triage and treatment.

Article activity feed

  1. Version published to 10.1183/13993003.01881-2021
  2. ScreenIT

    SciScore for 10.1101/2020.11.04.20225557: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Autopsy and biopsy materials were obtained from the Pontificia Universidade Catolica do Parana PUCPR the National Commission for Research Ethics (CONEP) under ethics approval numbers 2020001792/30188020.7.1001.0020 and 2020001934/30822820.8.000.0020.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Immunohistochemistry and RNAscope®: Immunohistochemistry was performed on a Leica Bond-RX autostainer (Leica Biosystems, US) with antibody targeting SARS-CoV-2 spike protein (Abcam, ab272504) at 2
    SARS-CoV-2 spike protein (Abcam,
    suggested: None
    Software and Algorithms
    SentencesResources
    Differential expression (DE) analysis, Gene Ontology and KEGG pathway enrichment analysis were carried out using R/Bioconductor packages edgeR (v3.30.3)28,29 and limma (v3.44.3)30.
    R/Bioconductor
    suggested: None
    edgeR
    suggested: (edgeR, RRID:SCR_012802)
    For these two contrasts, the voom-limma with duplicationCorrelations pipeline32 was used to fit linear models.
    duplicationCorrelations
    suggested: None
    Gene set enrichment analysis (GSEA) were performed using the fry approach from the limma package.
    Gene set enrichment analysis
    suggested: (Gene Set Enrichment Analysis, RRID:SCR_003199)
    Pathways from the KEGG pathway database were tested using the kegga function in the limma package and gene ontology enrichment was assessed using goana from the limma package31.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    limma
    suggested: (LIMMA, RRID:SCR_010943)

    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:
    This study was subject to several important limitations. Firstly, all data was derived from a small sample cohort derive from a single study site, and it remains to be determined how much these data can be extrapolated to other patient populations. Furthermore, additional studies are required across a broader range of patients (i.e. those with mild and moderate disease) to determine the therapeutic value of any of the putative tissue biomarkers identified herein. However, despite these limitations these data reveal the unprecedented power of spatial profiling combined with detailed multiparameter bioinformatic analyses to dissect the key variables that contribute to differential gene expression across highly variable patient cohorts and the heterogeneous distribution of virus and immune responsiveness within tissues. This study also demonstrated the value of using the suite of linear-modelling tools available in limma to interrogate the complex multi-factorial experiment design in this study. In particular, limma’s ability to model complex experimental designs and to implement information borrowing amongst genes to handle the relatively small number of samples in the panel, make it a highly attractive analysis tool for spatial profiling techniques with targeted gene panels. The present study suggests that spatial profiling would present many advantages in analysing COVID-19 samples across different patient cohorts to identify fundamental response signatures distinct from back...

    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

    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 10.1101/2020.11.04.20225557v1 on medRxiv