Tissue Proteomic Analysis Identifies Mechanisms and Stages of Immunopathology in Fatal COVID-19

Abstract

Immunopathology occurs in the lung and spleen in fatal coronavirus disease (COVID-19), involving monocytes/macrophages and plasma cells. Antiinflammatory therapy reduces mortality, but additional therapeutic targets are required. We aimed to gain mechanistic insight into COVID-19 immunopathology by targeted proteomic analysis of pulmonary and splenic tissues. Lung parenchymal and splenic tissue was obtained from 13 postmortem examinations of patients with fatal COVID-19. Control tissue was obtained from cancer resection samples (lung) and deceased organ donors (spleen). Protein was extracted from tissue by phenol extraction. Olink multiplex immunoassay panels were used for protein detection and quantification. Proteins with increased abundance in the lung included MCP-3, antiviral TRIM21, and prothrombotic TYMP. OSM and EN-RAGE/S100A12 abundance was correlated and associated with inflammation severity. Unsupervised clustering identified "early viral" and "late inflammatory" clusters with distinct protein abundance profiles, and differences in illness duration before death and presence of viral RNA. In the spleen, lymphocyte chemotactic factors and CD8A were decreased in abundance, and proapoptotic factors were increased. B-cell receptor signaling pathway components and macrophage colony stimulating factor (CSF-1) were also increased. Additional evidence for a subset of host factors (including DDX58, OSM, TYMP, IL-18, MCP-3, and CSF-1) was provided by overlap between 1) differential abundance in spleen and lung tissue; 2) meta-analysis of existing datasets; and 3) plasma proteomic data. This proteomic analysis of lung parenchymal and splenic tissue from fatal COVID-19 provides mechanistic insight into tissue antiviral responses, inflammation and disease stages, macrophage involvement, pulmonary thrombosis, splenic B-cell activation, and lymphocyte depletion.

Keywords: COVID-19; inflammation; lung; macrophages; proteomics.

Figure 1.

Proteomic analysis of lung parenchymal tissue in fatal coronavirus disease (COVID-19). (A) Differential protein abundance in patients with COVID-19 compared with control subjects. Volcano plot of log₂ fold change difference in protein abundance. Horizontal dotted line indicates false discovery rate of 0.05. Genes are colored based on differential abundance (false discovery rate [FDR] < 0.05): increased (orange), decreased (blue), or no difference (gray). (B) Patient-to-patient network analysis. Lung proteomic data was used to identify three clusters of patients. Edges represent connections with a Pearson correlation value of at least 0.85. The k-nearest neighbors method was used for edge reduction (k = 5). Nodes represent patients and are colored by cluster membership, determined using the Markov clustering algorithm (granularity 2.8). Plots below the network show differences in illness duration before death, presence of viral RNA, and inflammation severity between the two COVID-19 clusters. (C) Clustered heatmap of differential protein abundance. Clusters of proteins (columns) and patients (rows) were determined by hierarchical clustering (reproducing the same patient clusters as the Markov clustering method in panel [B]) and are represented by dendrograms. Metadata relating to each patient are shown by colored annotations: histological inflammation score of lung tissue used in analysis, presence/absence of viral RNA, and receipt of invasive mechanical ventilation (IMV) prior to death. Shading of cells represents the z-score, computed on a protein-by-protein basis. (D) Gene set enrichment of differentially abundant proteins in fatal COVID-19 lung parenchyma (FDR < 0.05) in the Gene Ontology Biological Processes database (no significantly enriched KEGG or WikiPathways pathways were identified). (E) Differential protein abundance in the late inflammatory cluster compared with the early viral cluster. Volcano plot of log₂ fold change difference in protein abundance. Horizontal dotted line indicates FDR of 0.05. Genes are colored based on differential abundance (FDR < 0.05): increased (orange), decreased (blue), or no difference (gray).

Figure 2.

Proteomic analysis of splenic tissue in fatal COVID-19. (A) Principal component analysis of 316 protein concentrations in splenic tissue from patients with fatal COVID-19 and deceased organ donors (control subjects). COVID-19 samples are shown in red and control samples in blue. (B) Differential protein abundance. Volcano plot of log₂ fold change difference in protein abundance between COVID-19 and control samples. Horizontal dotted line indicates FDR of 0.05. Genes are colored based on differential abundance (FDR < 0.05): increased (orange), decreased (blue), or no difference (gray). (C) Clustered heatmap of differential protein abundance. Clusters of proteins (columns) and patients (rows) were determined by hierarchical clustering and are represented by dendrograms. Viral presence is shown by colored annotation. Shading of cells represents the z-score, computed on a protein-by-protein basis. Gene set enrichment of differentially abundant proteins in fatal COVID-19 spleen tissue (FDR < 0.05) using the (D) Gene Ontology Biological Processes and (E) WikiPathways databases.

Figure 3.

Overlap of differentially abundant tissue proteins with other datasets. Venn diagrams (VennDiagram; R Studio) illustrating the overlap in (A) differentially abundant proteins between spleen and lung tissue from this study, (B) differentially abundant tissue proteins from this study and the top 500 results from COVID-19 meta-analysis by information content (MAIC), and (C) differentially abundant tissue proteins from this study (restricted to Olink Inflammation panel) and plasma proteins reported by Arunachalam and colleagues also using the Olink Inflammation panel. Overlapping proteins are listed to the right of the Venn diagrams.