Transcriptional Profiling of Immune and Inflammatory Responses in the Context of SARS-CoV-2 Fungal Superinfection in a Human Airway Epithelial Model

  1. Claire Nicolas de Lamballerie
  2. Andrés Pizzorno
  3. Julien Fouret
  4. Lea Szpiro
  5. Blandine Padey
  6. Julia Dubois
  7. Thomas Julien
  8. Aurélien Traversier
  9. Victoria Dulière
  10. Pauline Brun
  11. Bruno Lina
  12. Manuel Rosa-Calatrava
  13. Olivier Terrier

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Abstract

An increasing amount of evidence indicates a relatively high prevalence of superinfections associated with coronavirus disease 2019 (COVID-19), including invasive aspergillosis, but the underlying mechanisms remain to be characterized. In the present study, to better understand the biological impact of superinfection, we determine and compare the host transcriptional response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) versus Aspergillus superinfection, using a model of reconstituted human airway epithelium. Our analyses reveal that both simple infection and superinfection induce strong deregulation of core components of innate immune and inflammatory responses, with a stronger response to superinfection in the bronchial epithelial model compared to its nasal counterpart. Our results also highlight unique transcriptional footprints of SARS-CoV-2 Aspergillus superinfection, such as an imbalanced type I/type III IFN, and an induction of several monocyte and neutrophil associated chemokines, that could be useful for the understanding of Aspergillus-associated COVID-19 and also the management of severe forms of aspergillosis in this specific context.

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  1. Version published to 10.3390/microorganisms8121974
  2. ScreenIT

    SciScore for 10.1101/2020.05.19.103630: (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

    Experimental Models: Cell Lines
    SentencesResources
    Viral stocks were prepared and quantified in Vero E6 cells (TCID50/mL).
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    RNA-Seq data trimming: Raw reads were first cleaned with Cutadapt 2.8 (Martin, 2011) to trim adapters (AGATCGGAAGAGCACACGTCTGAACTCCAGTCA and AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT respectively for the first and the second reads) and low-quality ends (i.e terminal bases with phred quality score below 30).
    Cutadapt
    suggested: (cutadapt, RRID:SCR_011841)
    phred
    suggested: (Phred, RRID:SCR_001017)
    The reference transcriptome based on NCBI RefSeq annotation release 109.20191205 and genome assembly build GRCh37.p13 were chosen for this analysis (Maglott et al., 2005).
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    Raw counts were also computed at gene-level by sum and used as input for DESeq2 1.26.0 (Love et al., 2014).
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    The protein-protein interaction (PPI) network was analyzed with STRING 11.0 and visualized with Cytoscape 3.8.0.
    STRING
    suggested: (STRING, RRID:SCR_005223)
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)

    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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  3. Version published to 10.1101/2020.05.19.103630v1 on bioRxiv