The alarmin IL-33 exacerbates pulmonary inflammation and immune dysfunction in SARS-CoV-2 infection

Abstract

Dysregulated host immune responses contribute to disease severity and worsened prognosis in COVID-19 infection and the underlying mechanisms are not fully understood. In this study, we observed that IL-33, a damage-associated molecular pattern molecule, is significantly increased in COVID-19 patients and in SARS-CoV-2-infected mice. Using IL-33^-/- mice, we demonstrated that IL-33 deficiency resulted in significant decreases in bodyweight loss, tissue viral burdens, and lung pathology. These improved outcomes in IL-33^-/- mice also correlated with a reduction in innate immune cell infiltrates, i.e., neutrophils, macrophages, natural killer cells, and activated T cells in inflamed lungs. Lung RNA-seq results revealed that IL-33 signaling enhances activation of inflammatory pathways, including interferon signaling, pathogen phagocytosis, macrophage activation, and cytokine/chemokine signals. Overall, these findings demonstrate that the alarmin IL-33 plays a pathogenic role in SARS-CoV-2 infection and provides new insights that will inform the development of effective therapeutic strategies for COVID-19.

Keywords: Immunology; Molecular network; Transcriptomics; Virology.

Graphical abstract

Figure 1

SARS-CoV-2 infection leads to increased IL-33 levels and pulmonary inflammation (A) IL-33 levels in human plasma and naso-/oro-pharyngeal swabs were higher in COVID-19 patients. Patient demographics, clinical status, and numbers in each group are shown in Table S2. (B) B6 mice (n = 4/group) were i.n. inoculated intranasally (i.n.) with 5 × 10⁵ PFU SARS-CoV-2 CMA3p20. Lung tissues and sera were harvested at D2 and D4. Mock mice received an inoculation of virus-culture medium. Relative fold changes in the gene expression of cytokines (Ifnb1, Ifng, Tnf, Il6, Il1b, Il10, and Csf1) and chemokines (Cxcl1, Cxcl2, and Ccl2) within lung tissues were determined by RT-qPCR. (C) Serum cytokine and chemokine levels were measured by Bio-Plex assay. The heatmap was generated using fold-changes which were calculated in comparison to mock samples. (D) Lung IL-33 transcript and protein levels were determined by RT-qPCR and western blot, respectively. Human data were analyzed by nonparametric analysis, followed by Dunn’s multiple comparisons test. Mouse bodyweight change was analyzed using repeated measures two-way ANOVA. Pairwise comparisons were performed by Tukey’s multiple comparisons test at each time point. All other mouse data were analyzed by one-way ANOVA, followed by Tukey’s multiple comparison test. The results are presented as the mean ± SD, and the animal experiment was performed twice independently. Statistically significant values are denoted as ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, and ∗∗∗∗p < 0.0001.

Figure 2

IL-33 deficiency alleviated SARS-CoV-2-induced pulmonary inflammation in mice WT B6 and IL-33^−/− mice were infected with SARS-CoV-2 as indicated in Figure 1. B6 mice in the mock group were i.n. treated with the same volume of cell culture medium. (A) Bodyweight changes of WT and IL-33^−/− mice after SARS-CoV-2 infection. Data were pooled from two independent experiments and were analyzed by repeated measures two-way ANOVA. (B) Relative viral load was measured by RT-qPCR; bar graph represents as a mean ± SD (n = 7–8/group). These data were analyzed by t tests at each time point. (C) Histological changes in the lungs of WT and IL-33^−/− mice after SARS-CoV-2 infection: immune cell infiltration (black arrow), intrabronchial mucus and cell debris (blue arrows) and epithelial regeneration (red arrows). (D) Gating strategy flow cytometry data. Lymphocytes were selected from a forward scatter-A vs. side scatter-A dot plot, and single cells were subsequently selected. Then, live lymphocytes were selected by live/dead dye and CD45. Neutrophils were identified as Ly6G⁺ cells. Macrophages were identified as CD11b⁺CD64⁺ cells. T cells were first identified as CD3⁺ and further subdivided into CD4⁺ and CD8⁺ T cells. NK cells were identified as CD3⁻ and NK1.1⁺. γδT cells were identified as CD3⁺ and γδTCR⁺. CD44⁺CD69⁺ population was characterized as activated cells. (E) Flow cytometric analysis of lung tissues of WT and IL-33^−/− mice at days 2 and 4 post-SARS-CoV-2 infection. The numbers of neutrophils, activated NK cells, macrophages, activated CD4 T cells, activated CD8 T cells and activated γδ T cells were plotted. Bar graph represents as a mean ± SD (n = 4–5/group) (one-way ANOVA followed by Tukey’s multiple comparison test at each time point). (F) Protein levels of IL-5, IL-6, IL-12p40, IFN-g, G-CSF, CXCL1, CCL2, and CCL-11 in the serum of WT and IL-33^−/− mice after SARS-CoV-2 infection. Data were analyzed by unpaired/two-tailed t tests at each time point. All results are presented as the mean ± SD, and this experiment was performed twice independently with similar trends. Statistically significant values are denoted as ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, and ∗∗∗∗p < 0.0001.

Figure 3

Exogenous IL-33 exacerbated SARS-CoV-2-induced lung inflammation and pathology (A) Schematic of SARS-CoV-2 infection (Created with BioRender.com). B6 mice were i.n. infected with 5 × 10⁵ PFU CMA3p20, followed by intraperitoneal administration of PBS or recombinant IL-33 (rIL-33). Uninfected mice with or without rIL-33 treatment were used as controls. Lung tissues were harvested at D2 and D4. The illustration was created with BioRender.com. (B) Bodyweight changes in mice post SARS-CoV-2 infection and treatment. Data were pooled from two independent experiments and were analyzed by repeated measures two-way ANOVA. (C) Lung viral loads were measured by RT-qPCR and these data were analyzed by unpaired/two-tailed t tests at each time point. (D) Representative H&E staining images of lungs. Scale bars, 100 μm. Immune cell infiltration into alveolar spaces (lung consolidation), chronic fibrosis and bronchial epithelial regeneration (arrows) were observed in lung sections from SARS-CoV-2 infected mice. (E) Relative transcript levels of the indicated genes in the lungs at D4 were analyzed by RT-qPCR (n = 4–5/group). These data were analyzed by one-way ANOVA, followed by Tukey’s multiple comparison test. All results are presented as the mean ± SD, and this experiment was performed twice independently with similar trends. Statistically significant values are denoted as ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, and ∗∗∗∗p < 0.0001.

Figure 4

IL-33 regulated inflammatory signaling pathways in SARS-CoV-2-infection WT B6 and IL-33^−/− mice were infected with SARS-CoV-2 as indicated in Figure 1 (n = 3–5 mice/group). B6 mice in the mock group were i.n. treated with the same volume of cell culture medium. Lungs were perfused with cold PBS and harvested for RNA extraction and RNA-seq analysis. (A) Volcano plot describing the fold changes and false discovery rate (FDR)-adjusted p values between infected WT and IL-33^−/− lungs on D2. (B) Pathway enrichment analysis of top ten hallmark pathways in the lungs on D2. (C) Representative heatmaps of differently expressed genes of IFN-γ signaling pathway in the lungs of WT and IL-33^−/− mice on D2. Heatmaps were generated based on KEGG pathway using the Rosalind platform. (D) Network plot showing the genes correlating IL-33 to the COVID-19 disease database identified by ingenuity pathway analysis of the bulk RNA-seq. The orange and green labels indicate upregulation and downregulation, respectively, when compare IL-33^−/− to WT samples. (E) The network of differently expressed genes, pathways and cell functions analyzed by IPA network analyzer. The orange and blue legend on the right indicates the genes predicted to be activation or inhibition, respectively.