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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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 Sentences Resources 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. NetworkAnalystsuggested: (NetworkAnalyst, RRID:SCR_016909)Shared DEGs among all datasets were displayed using Venn diagram116 and Circos Plot117 online tools. Circossuggested: (Circos, RRID:SCR_011798)We followed the Seurat pipeline118 as previously … 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 Sentences Resources 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. NetworkAnalystsuggested: (NetworkAnalyst, RRID:SCR_016909)Shared DEGs among all datasets were displayed using Venn diagram116 and Circos Plot117 online tools. Circossuggested: (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. Seuratsuggested: (SEURAT, RRID:SCR_007322)Then, the resultant network was annotated, analysed, and visualized using NAViGaTOR 3.0.14120 NAViGaTORsuggested: (TMA Navigator, RRID:SCR_005599)Enrichment analysis and data visualization: We used ClusterProfiler122 R package to obtain dot plots of enriched signaling pathways. ClusterProfiler122suggested: NoneElsevier 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. Enrichrsuggested: (Enrichr, RRID:SCR_001575)Circular heatmaps were generated using R version 4.0.5 (The R Project for Statistical Computing. R Project for Statisticalsuggested: (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)circlizesuggested: (circlize, RRID:SCR_002141)We trained a Random Forest model using the functionalities of the R package randomForest (version 4.6.14)79. randomForestsuggested: (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. ggplot2suggested: (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.
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- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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