Systems Immunology Analysis Reveals the Contribution of Pulmonary and Extrapulmonary Tissues to the Immunopathogenesis of Severe COVID-19 Patients

Abstract

As one of the current global health conundrums, COVID-19 pandemic caused a dramatic increase of cases exceeding 79 million and 1.7 million deaths worldwide. Severe presentation of COVID-19 is characterized by cytokine storm and chronic inflammation resulting in multi-organ dysfunction. Currently, it is unclear whether extrapulmonary tissues contribute to the cytokine storm mediated-disease exacerbation. In this study, we applied systems immunology analysis to investigate the immunomodulatory effects of SARS-CoV-2 infection in lung, liver, kidney, and heart tissues and the potential contribution of these tissues to cytokines production. Notably, genes associated with neutrophil-mediated immune response (e.g. CXCL1) were particularly upregulated in lung, whereas genes associated with eosinophil-mediated immune response (e.g. CCL11) were particularly upregulated in heart tissue. In contrast, immune responses mediated by monocytes, dendritic cells, T-cells and B-cells were almost similarly dysregulated in all tissue types. Focused analysis of 14 cytokines classically upregulated in COVID-19 patients revealed that only some of these cytokines are dysregulated in lung tissue, whereas the other cytokines are upregulated in extrapulmonary tissues (e.g. IL6 and IL2RA). Investigations of potential mechanisms by which SARS-CoV-2 modulates the immune response and cytokine production revealed a marked dysregulation of NF-κB signaling particularly CBM complex and the NF-κB inhibitor BCL3. Moreover, overexpression of mucin family genes (e.g. MUC3A, MUC4, MUC5B, MUC16, and MUC17) and HSP90AB1 suggest that the exacerbated inflammation activated pulmonary and extrapulmonary tissues remodeling. In addition, we identified multiple sets of immune response associated genes upregulated in a tissue-specific manner (DCLRE1C, CHI3L1, and PARP14 in lung; APOA4, NFASC, WIPF3, and CD34 in liver; LILRA5, ISG20, S100A12, and HLX in kidney; and ASS1 and PTPN1 in heart). Altogether, these findings suggest that the cytokines storm triggered by SARS-CoV-2 infection is potentially the result of dysregulated cytokine production by inflamed pulmonary and extrapulmonary (e.g. liver, kidney, and heart) tissues.

Keywords: COVID19; SARS-CoV-2; cytokine storm; extrapulmonary tissues; heart; immunopathogenesis; kidney; liver.

Figure 1

(A) Count of differentially expressed immune response genes from the immune response gene ontology set (GO:0006955) in SARS-CoV-2 infected lung, liver, kidney, and heart tissues. Venn diagram representation of the overlap of significantly (B) upregulated and (C) downregulated immune response genes amongst the four types of infected tissues. Functional clustering and pathway analysis of the commonly (D) upregulated and (E) downregulated genes across lung, liver, kidney, and heart tissues in response to SARS-CoV-2 infection.

Figure 2

(A) dotplot representation of the expression fold change of cytokines contributing to the cytokine storm in response to SARS-CoV-2 infection in lung, liver, kidney, and heart tissues. Dotplot representation of the differentially expressed (B) chemokines, (C) interleukins, (D) interferons and tumor necrosis factor family members in lung, liver, kidney, and heart tissues infected with SARS-CoV-2.

Figure 3

The count of differentially expressed genes in SARS-CoV-2 infected lung, liver, kidney, and heart tissues overlapping with the (A) immune cells chemotaxis gene ontology set, (B) immune cells activation gene ontology sets, and (C) Immunome gene ontology set ontology sets.

Figure 4

dotplot representation of the expression fold change of (A) inducible NF-κB genes and (B) CBM complex and CARD family proteins in lung, liver, kidney, and heart tissues infected with SARS-CoV-2. Red arrows representing core elements of the NF-κB signaling pathways.

Figure 5

(A) Top 30 upregulated immune response genes in lung, liver, kidney, and heart tissues in response to SARS-CoV-2 infection; F.C.: fold change; light red represents commonly upregulated genes within the top 20 genes across the 4 types of tissues. (B) Venn diagram representation of the overlap between the top 20 upregulated immune response genes in each tissue, enlisted in (A). (C, D) Top Immune response genes uniquely upregulated in lung, liver, kidney, and heart tissues in response to SARS-CoV-2 infection. * represents p-value < 0.05; ** represents p-value < 0.01 analyzed using T-test statistical analysis.

Figure 6

(A) Gene expression cross-validation of identified uniquely and commonly expressed immune putative biomarkers across pulmonary and extrapulmonary tissues using RNA sequencing dataset of whole blood samples from healthy donors and asymptomatic, mild, and severe COVID-19 patients. (B) Gene expression cross-validation of commonly expressed immune putative biomarkers across pulmonary and extrapulmonary tissues using qRT-PCR analysis of blood plasma samples from 3 healthy donors as well as 6 asymptomatic COVID-19 patients, 2 severe COVID-19 patients with pulmonary findings (pulmonary findings) only, and 4 severe COVID-19 with pulmonary and extrapulmonary finding (extrapulmonary findings). * represents p-value < 0.05; ** represents p-value < 0.01; *** represents p-value < 0.001; analyzed using unpaired t-test statistical analysis.

Figure 7

SARS-CoV-2 may target extrapulmonary tissues along pulmonary tissues thereby inducing transcriptional shifts in the expression of central immune regulators (examples in the blue boxes; putative biomarkers unique for each tissue). Consequently, pulmonary as well as extrapulmonary tissues may secrete cytokines and immune mediators (in blue font) resulting in the chemoattraction and activation of innate and adaptive immune cells (e.g. Neutrophils, eosinophils, monocytes, dendritic cells, T-cells and B-cells). Upon activation, the infiltrating immune cells begin to uncontrollably secrete additional cytokines and immune mediators which elicits the cytokine storm and system inflammation phenomena.