Persistence is key: unresolved immune dysfunction is lethal in both COVID-19 and non-COVID-19 sepsis

Abstract

Introduction: Severe COVID-19 and non-COVID-19 pulmonary sepsis share pathophysiological, immunological, and clinical features, suggesting that severe COVID-19 is a form of viral sepsis. Our objective was to identify shared gene expression trajectories strongly associated with eventual mortality between severe COVID-19 patients and contemporaneous non-COVID-19 sepsis patients in the intensive care unit (ICU) for potential therapeutic implications.

Methods: Whole blood was drawn from 20 COVID-19 patients and 22 non-COVID-19 adult sepsis patients at two timepoints: ICU admission and approximately a week later. RNA-Seq was performed on whole blood to identify differentially expressed genes and significantly enriched pathways. Using systems biology methods, drug candidates targeting key genes in the pathophysiology of COVID-19 and sepsis were identified.

Results: When compared to survivors, non-survivors (irrespective of COVID-19 status) had 3.6-fold more "persistent" genes (genes that stayed up/downregulated at both timepoints) (4,289 vs. 1,186 genes); these included persistently downregulated genes in T-cell signaling and persistently upregulated genes in select innate immune and metabolic pathways, indicating unresolved immune dysfunction in non-survivors, while resolution of these processes occurred in survivors. These findings of persistence were further confirmed using two publicly available datasets of COVID-19 and sepsis patients. Systems biology methods identified multiple immunomodulatory drug candidates that could target this persistent immune dysfunction, which could be repurposed for possible therapeutic use in both COVID-19 and sepsis.

Discussion: Transcriptional evidence of persistent immune dysfunction was associated with 28-day mortality in both COVID-19 and non-COVID-19 septic patients. These findings highlight the opportunity for mitigating common mechanisms of immune dysfunction with immunomodulatory therapies for both diseases.

Keywords: COVID-19; drug repurposing; gene expression; immune dysfunction; sepsis.

Figure 1

Non-survivors had substantially more persistent genes than survivors. (A) Number of differentially expressed genes (DEGs) at either Day 1 (D1) or Day 7 (D7) in the ICU for non-survivors and survivors, compared to healthy controls. The fraction of persistent DEGs (DEGs that were up/down-regulated at both timepoints) among all DEGs is highlighted. (B) Examples of persistently upregulated (PCSK9) or persistently downregulated (ZAP70) genes only found in non-survivors are shown. Empty circles indicate that the gene was no longer a DEG at that timepoint. (C) Eventually deceased patients had persistent dysfunction of immune and cellular pathways. A subset of enriched Reactome pathways and Hallmark gene sets from persistently upregulated (Δ) and downregulated (∇) genes in patients who eventually were deceased or survived are shown. The total numbers of persistent genes in each comparison are under each label. All enriched pathways and gene sets can be found in Figures S5 and S6 . Pathway plots were generated using pathlinkR (https://github.com/hancockinformatics/pathlinkR).

Figure 2

Eventually deceased patients had unresolving immune dysfunction. A subset of significantly enriched Reactome pathways (top) and Hallmark gene sets (bottom) using differentially expressed (DE) genes from comparing eventually deceased or surviving patients to healthy controls at Day 1 (D1) and Day 7 (D7). The total numbers of DE genes in each comparison are under each label. All enriched pathways and gene sets shown in Figures S6 and S7 . Pathway plots were generated using pathlinkR.

Figure 3

Resolution of inflammation and adaptive suppression occurred only in survivors. (A) A subset of significantly enriched Reactome pathways and Hallmark gene sets using differentially expressed (DE) genes over time. The total numbers of DE genes in each comparison are under each label. All enriched pathways and gene sets shown in Figures S6 and S7 . For one pathway, both directions were enriched (indicated by *); the direction with the lower adjusted p-value (more significantly enriched) is shown. (B) Persistent suppression of T cell signaling genes was observed in non-survivors. D = deceased, S = survived, HC = healthy controls. Shading in the heatmap represents fold change. Only DE genes are shown. Significance values are derived from DESeq2 results: *** = p<0.001, ** = p<0.01, and * = p<0.05. Pathway plots and heatmaps were generated using pathlinkR. (C) Network analysis of DE genes over time in survivors and non-survivors. Zero-order (i.e., only dysregulated nodes) functional protein-protein interactions (PPI) networks were drawn using NetworkAnalyst. Dots represent nodes (genes and their protein products) and are colored for directionality. Lines joining the dots represent known PPI from InnateDB. Differential expression of TCR signaling genes was not seen in non-survivors but these genes were upregulated over time in survivors (boxed area).

Figure 4

Non-survivors had persistent enrichment of the mortality signature and were “locked-in” to more severe sepsis endotypes. (A) The gene set variation analysis (GSVA) enrichment score of our published 38-gene mortality signature (9) was significantly higher in eventually deceased patients compared to survivors at both D1 and D7 (Wilcoxon rank-sum test, * = p<0.05). (B) Fold changes of mortality signature genes in eventually deceased patients and survivors relative to healthy controls. Shading in the heatmap represents fold change. Only DE genes are shown. Non-survivors had 15 persistently dysregulated (differentially expressed at D1 and D7) mortality signature genes compared to only 3 in survivors. (C) Eventually deceased patients were “locked in” to severe endotypes. Five endotypes were derived from differentially expressed genes in a cohort of emergency room sepsis patients: Neutrophilic-Suppressive (NPS), Inflammatory (INF), Interferon (IFN), Adaptive (ADA), and Innate Host Defense (IHD) (9). Each patient was classified into an endotype based on which of the five endotypes had the highest GSVA enrichment score. An alluvial graph demonstrating transition of each patient’s endotype between D1 and D7 is shown. (D) NPS GSVA enrichment scores were persistently high in eventually deceased patients but significantly decreased in survivors (pair-wise Wilcoxon rank-sum test, * = p<0.05; ** = p<0.01, *** = p<0.001, **** = p<0.0001, ns = not significant).

Figure 5

Protein interaction network of persistent genes in deceased patients highlight potential hub genes for drug targeting. Zero-order (i.e., only dysregulated nodes) functional protein-protein interactions (PPI) networks were drawn using NetworkAnalyst. Dots represent nodes (genes and their protein products) and are coloured red for upregulated and green for downregulated. Lines joining the dots represent known PPI from InnateDB. Hub genes, which are genes with multiple interactions, are displayed as the largest nodes (size related to hub degree) and are labelled. Hub genes are attractive targets for drugs as they are expected to regulate or interact with multiple other dysregulated genes and proteins during severe disease. Drugs with known interactions with the top 15 upregulated hubs (circled in blue) are listed in Table 3 .