Severe COVID-19 Shares a Common Neutrophil Activation Signature with Other Acute Inflammatory States

  1. Lena F. Schimke
  2. Alexandre H. C. Marques
  3. Gabriela Crispim Baiocchi
  4. Caroline Aliane de Souza Prado
  5. Dennyson Leandro M. Fonseca
  6. Paula Paccielli Freire
  7. Desirée Rodrigues Plaça
  8. Igor Salerno Filgueiras
  9. Ranieri Coelho Salgado
  10. Gabriel Jansen-Marques
  11. Antonio Edson Rocha Oliveira
  12. Jean Pierre Schatzmann Peron
  13. Gustavo Cabral-Miranda
  14. José Alexandre Marzagão Barbuto
  15. Niels Olsen Saraiva Camara
  16. Vera Lúcia Garcia Calich
  17. Hans D. Ochs
  18. Antonio Condino-Neto
  19. Katherine A. Overmyer
  20. Joshua J. Coon
  21. Joseph Balnis
  22. Ariel Jaitovich
  23. Jonas Schulte-Schrepping
  24. Thomas Ulas
  25. Joachim L. Schultze
  26. Helder I. Nakaya
  27. Igor Jurisica
  28. Otávio Cabral-Marques

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Severe COVID-19 patients present a clinical and laboratory overlap with other hyperinflammatory conditions such as hemophagocytic lymphohistiocytosis (HLH). However, the underlying mechanisms of these conditions remain to be explored. Here, we investigated the transcriptome of 1596 individuals, including patients with COVID-19 in comparison to healthy controls, other acute inflammatory states (HLH, multisystem inflammatory syndrome in children [MIS-C], Kawasaki disease [KD]), and different respiratory infections (seasonal coronavirus, influenza, bacterial pneumonia). We observed that COVID-19 and HLH share immunological pathways (cytokine/chemokine signaling and neutrophil-mediated immune responses), including gene signatures that stratify COVID-19 patients admitted to the intensive care unit (ICU) and COVID-19_nonICU patients. Of note, among the common differentially expressed genes (DEG), there is a cluster of neutrophil-associated genes that reflects a generalized hyperinflammatory state since it is also dysregulated in patients with KD and bacterial pneumonia. These genes are dysregulated at the protein level across several COVID-19 studies and form an interconnected network with differentially expressed plasma proteins that point to neutrophil hyperactivation in COVID-19 patients admitted to the intensive care unit. scRNAseq analysis indicated that these genes are specifically upregulated across different leukocyte populations, including lymphocyte subsets and immature neutrophils. Artificial intelligence modeling confirmed the strong association of these genes with COVID-19 severity. Thus, our work indicates putative therapeutic pathways for intervention.

Article activity feed

  1. Version published to 10.3390/cells11050847
  2. Version published to 10.1101/2021.07.30.454529v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.07.30.454529: (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
    Differential expression analysis, meta-analysis and visualization of multiple gene expression data sets from microarray and bulk RNAseq: Read counts were transformed (log2 count per million or CPM) and differentially expressed genes (DEGs) between groups were identified through the webtool NetworkAnalyst 3.0114 using limma-voom pipeline115.
    NetworkAnalyst
    suggested: (NetworkAnalyst, RRID:SCR_016909)
    Shared DEGs among all datasets were displayed using Venn diagram116 and Circos Plot117 online tools.
    Circos
    suggested: (Circos, RRID:SCR_011798)
    We followed the Seurat pipeline118 as previously described by Stuart et al.119 to perform differential expression analysis and data visualization, i.e., UMAP, dotplot, and heatmap construction.
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    Then, the resultant network was annotated, analysed, and visualized using NAViGaTOR 3.0.14120
    NAViGaTOR
    suggested: (TMA Navigator, RRID:SCR_005599)
    Enrichment analysis and data visualization: We used ClusterProfiler122 R package to obtain dot plots of enriched signaling pathways.
    ClusterProfiler122
    suggested: None
    Elsevier Pathway Collection analysis for selected gene lists (7 genes underlying fHLH/IEI and 11 genes associated with severe COVID-19) was carried out using Enrichr webtool123–125.
    Enrichr
    suggested: (Enrichr, RRID:SCR_001575)
    Circular heatmaps were generated using R version 4.0.5 (The R Project for Statistical Computing.
    R Project for Statistical
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)
    RStudio. https://www.rstudio.com/) using the circlize R package.
    https://www.rstudio.com/
    suggested: (RStudio, RRID:SCR_000432)
    circlize
    suggested: (circlize, RRID:SCR_002141)
    We trained a Random Forest model using the functionalities of the R package randomForest (version 4.6.14)79.
    randomForest
    suggested: (RandomForest Package in R, RRID:SCR_015718)
    Differences in protein expression between COVID-19_ICU and COVID-19_nonICU was calculated using the nonparametric MANOVA (multivariate analysis of variance) test129 followed by analysis of nonparametric Inference for Multivariate Data130 using the R packages npmv, nparcomp, and ggplot2.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    Results from OddPub: Thank you for sharing your code and 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 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.

    Results from scite Reference Check: We found no unreliable references.

    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.

  4. Version published to 10.1101/2021.07.30.454529v1 on bioRxiv