Distinct pulmonary and systemic effects of dexamethasone in severe COVID-19

Abstract

Dexamethasone is the standard of care for critically ill patients with COVID-19, but the mechanisms by which it decreases mortality and its immunological effects in this setting are not understood. Here we perform bulk and single-cell RNA sequencing of samples from the lower respiratory tract and blood, and assess plasma cytokine profiling to study the effects of dexamethasone on both systemic and pulmonary immune cell compartments. In blood samples, dexamethasone is associated with decreased expression of genes associated with T cell activation, including TNFSFR4 and IL21R. We also identify decreased expression of several immune pathways, including major histocompatibility complex-II signaling, selectin P ligand signaling, and T cell recruitment by intercellular adhesion molecule and integrin activation, suggesting these are potential mechanisms of the therapeutic benefit of steroids in COVID-19. We identify additional compartment- and cell- specific differences in the effect of dexamethasone that are reproducible in publicly available datasets, including steroid-resistant interferon pathway expression in the respiratory tract, which may be additional therapeutic targets. In summary, we demonstrate compartment-specific effects of dexamethasone in critically ill COVID-19 patients, providing mechanistic insights with potential therapeutic relevance. Our results highlight the importance of studying compartmentalized inflammation in critically ill patients.

Fig. 1. Dexamethasone modulates cytokine and immune cell gene expression in the blood of patients with COVID-19.

a The introduction of dexamethasone (Dex) as standard of care for critically ill patients hospitalized with COVID-19 based on the results of the RECOVERY trial. Blood and tracheal aspirate (TA) samples were collected from intubated patients enrolled either before or after this change. Figure 1a Created with BioRender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en). b Included patients and time points per analysis. A single sample was used per patient. Each patient was either treated with Dex (orange) or not (blue). Samples used in DIABLO analysis (Fig. 2) are the overlap in PBMC bulk RNA sequencing and plasma cytokine rows. c Individual plots of log-transformed significant cytokines IL-6, IL-10, and interferon gamma (IFN-gamma) (two-sided Wilcoxon rank-sum test, BH-adjusted p < 0.1). The median, first and third quartiles, and 1.5*interquartile range distance from the quartiles are represented using the center mark, hinges, and whiskers, respectively. N = 23 Dex, N = 15 NoDex. d Volcano plot of differential gene expression of PBMC RNA-seq data with DESeq2 (based on two-sided negative binomial generalized linear models). Significance determined using a BH-adjusted p < 0.1. N = 10 Dex, N = 11 NoDex.

Fig. 2. Supervised integrative analysis of blood transcriptomic and plasma cytokine data captures co-varying effects of dexamethasone on immune cell pathways and modulators.

a Integrative analysis of plasma cytokines (17 cytokine variables) and bulk PBMC RNA-seq (500 gene variables) data (paired) from patients comparing Dex and NoDex using DIABLO and highlighting shared contributions from individual data modalities. N = 10 Dex, N = 11 NoDex; day 0 of hospitalization. First two variates from DIABLO run comparing Dex (orange) vs. NoDex (blue) samples. A parameter value of 0.5 was chosen to model the strength of the relationship between the data and the treatment status. b Cytokine contribution (loadings) to DIABLO variate 1. The color indicates the treatment group in which the median value was the highest (orange for Dex and blue for NoDex). c GeneNet enrichment scores (NES) from gene set enrichment analysis (one-sided test based on a modified Kolmogorov–Smirnov statistic) of PBMC RNA-seq contribution to DIABLO variate 1 (loadings) using REACTOME gene sets (methods). 20 most significant terms (BH-adjusted p < 0.1) represented: top 10 for Dex (orange) and top 10 for NoDex (blue).

Fig. 3. Single-cell analysis of lung and peripheral blood samples from patients treated with or without dexamethasone.

Plot per patient showing the collection of whole blood (WB) (a N = 7 Dex, 3 NoDex) or tracheal aspirate (TA) samples (b N = 10 Dex, 7 NoDex) overlaid on hospitalization (gray bars) and dexamethasone treatment (pink bars). X-axis shows days of hospitalization (day 0 = admission to UCSF hospital). Dots show the day when sample was collected, colored by Study Day (methods). UMAP plots of single-cell RNA-seq data from blood (c) or TA (d) samples, clustered and annotated by major immune cell types. UMAP plots of single-cell RNA-seq data from blood (e) or TA (f) samples, colored by Dex (blue) or NoDex (pink) samples. log₂ fold difference of gene expression of Dex and NoDex in TA (y-axis) v. blood (x-axis) plotted for Neutrophils (g) and Tregs (h). Significant genes in TA only (blue), blood only (brown), both compartments (red) are shown (BH-adj. p < 0.1 & |log₂ fold-difference | > 0.5). Spearman’s correlation R value shown between the two compartments.

Fig. 4. Dexamethasone has discordant effects on cell type specific gene expression in lung and peripheral blood that are reproducible in external datasets.

Net enrichment scores from gene set enrichment analysis in blood (a) and lung (b), faceted by cell type. Orange circles have a positive net enrichment score (NES), indicating the pathway is more highly expressed in dexamethasone-treated COVID-19 patients (Dex) or healthy controls relative to NoDex subjects. Solid circles identify pathways where GSEA BH-adjusted p < 0.1, empty circles identify pathways with GSEA BH-adjusted p ≥ 0.1, and blank spaces indicate no GSEA NES score was calculated for that pathway. Significance was determined using a one-sided test based on a modified Kolmogorov–Smirnov statistic. Datasets represented are from COMET (whole blood, TA), Sinha et al. (blood) and Liao et al. (BAL). Ns reported in Fig. S8 and Supplementary Data File 2.

Fig. 5. Receptor ligand inference from single-cell sequencing data reveals decrease in inflammation, antigen presentation, and T cell recruitment in blood and lung in response to dexamethasone.

a Clustered heatmap of CellChat results of TA samples from Dex (N = 10) and NoDex (N = 7) patients with significant receptor-ligand pairs shown (based on one-sided Wilcoxon signed rank test (BH-adj. p < 0.1 and |log₂ fold-difference | > 1). Cell type interaction networks for MHC-II (b) and SELPLG interactions (c) shown comparing NoDex (left, N = 7) and Dex (right, N = 10) patients of TA samples. Line thickness represents predicted strength of the interaction. d Clustered heatmap of CellChat results of blood samples from Dex (COMET), Dex (Sinha et al.), NoDex (COMET), NoDex (Sinha et al.), and healthy controls (COMET) with receptor-ligand pairs that are significant between at least one pair of patient groups are shown (based on one-sided Wilcoxon signed rank test (BH-adj. p < 0.1 and | log₂ fold-difference | > 1). e Comparisons for the COMET dataset shown between Dex, NoDex, and healthy controls for a subset of significantly detected receptor-ligand interactions (*adj. p < 0.1, **adj. p < 0.001, ***adj. p < 0.0001, ****adj. p < 0.00001; BH-adjusted). Ns reported in Fig. S8 and Supplementary Data File 2.