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

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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.

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  1. 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 …