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. 2020 Jun 26:11:1636.
doi: 10.3389/fimmu.2020.01636. eCollection 2020.

Immune and Metabolic Signatures of COVID-19 Revealed by Transcriptomics Data Reuse

Luiz G Gardinassi  1 , Camila O S Souza  2 , Helioswilton Sales-Campos  1 , Simone G Fonseca  1
Affiliations

Immune and Metabolic Signatures of COVID-19 Revealed by Transcriptomics Data Reuse

Luiz G Gardinassi et al. Front Immunol. .

Abstract

The current pandemic of coronavirus disease 19 (COVID-19) has affected millions of individuals and caused thousands of deaths worldwide. The pathophysiology of the disease is complex and mostly unknown. Therefore, identifying the molecular mechanisms that promote progression of the disease is critical to overcome this pandemic. To address such issues, recent studies have reported transcriptomic profiles of cells, tissues and fluids from COVID-19 patients that mainly demonstrated activation of humoral immunity, dysregulated type I and III interferon expression, intense innate immune responses and inflammatory signaling. Here, we provide novel perspectives on the pathophysiology of COVID-19 using robust functional approaches to analyze public transcriptome datasets. In addition, we compared the transcriptional signature of COVID-19 patients with individuals infected with SARS-CoV-1 and Influenza A (IAV) viruses. We identified a core transcriptional signature induced by the respiratory viruses in peripheral leukocytes, whereas the absence of significant type I interferon/antiviral responses characterized SARS-CoV-2 infection. We also identified the higher expression of genes involved in metabolic pathways including heme biosynthesis, oxidative phosphorylation and tryptophan metabolism. A BTM-driven meta-analysis of bronchoalveolar lavage fluid (BALF) from COVID-19 patients showed significant enrichment for neutrophils and chemokines, which were also significant in data from lung tissue of one deceased COVID-19 patient. Importantly, our results indicate higher expression of genes related to oxidative phosphorylation both in peripheral mononuclear leukocytes and BALF, suggesting a critical role for mitochondrial activity during SARS-CoV-2 infection. Collectively, these data point for immunopathological features and targets that can be therapeutically exploited to control COVID-19.

Keywords: COVID-19; SARS-CoV; SARS-CoV-2; inflammation; influenza; metabolism; oxidative phosphorylation; transcriptomics.

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Figures

Figure 1
Figure 1
COVID-19 induces the differential activity of gene modules underlying immune cells. (A) BTM association with the transcriptional profile of PBMCs from COVID-19 patients (RNA-seq dataset CRA002390) was determined with gene set enrichment analysis (GSEA), with 1,000 permutations and weighted enrichment statistics. The gene list was pre-ranked by Wald statistic scores derived from DESeq2 output. Nodes in the network indicate BTMs reaching a significance of FDR adjusted p < 0.001. Colors represent the normalized enrichment scores (NES) of each BTM. Width of edges represent the number of genes shared by two BTMs. (B) Representative network of the BTM enriched in monocyte (M11.0). Colors represent log2 fold changes of each gene in the transcriptome of COVID-19 patients compared to healthy controls. (C,D) Heat maps representing the differential expression signatures of genes enriched in (C) dendritic cells (M168) and genes enriched in (D) natural killer (NK) cells I (M7.2), between COVID-19 patients and healthy controls.
Figure 2
Figure 2
Modular transcriptional profiles of SARS-CoV-2 infection compared to SARS-CoV-1 or IAV. (A) The BTM-driven meta-analysis was based on over 600 human transcriptome samples including: SARS-CoV-2 (CRA002390-PBMC), SARS-CoV-1 (GSE1739-PBMC), Influenza (IAV)-PBMC (GSE34205, GSE6269), and IAV-whole blood (GSE29366, GSE38900, GSE20346, GSE52428, GSE40012, GSE68310, GSE61754, GSE90732). Gene lists were pre-ranked by log2 fold change of experimental samples over healthy controls and used as input in GSEA, with BTMs as gene sets, 1000 permutations and weighted enrichment statistics. BTMs reaching a significance of nominal p < 0.001 and associated with at least 50% of the datasets are shown. Colors represent the normalized enrichment scores (NES), reflecting negative (blue) or positive (red) regulation. Gray color indicates that difference was not significant. Each dataset was specified by ID, virus and sample type in the heat map (B) Expression of type I interferon-related genes in whole blood of an independent cohort of COVID-19 patients and analytical platform (E-MTAB-8871) (8). (C) BMTs specifically enriched in PBMCs from COVID-19 patients (FDR adjusted p < 0.01). (D) Representative network of the heme biosynthesis II (M222) module. Colors represent log2 fold changes of each gene in the transcriptome of COVID-19 patients compared to healthy controls. (E) Metabolic pathways enriched in the transcriptome of PBMCs from COVID-19 patients. Genes were pre-ranked by log2 fold change of COVID-19 patients over healthy controls and used as input in GSEA, with KEGG pathways as gene sets, 1,000 permutations and weighted enrichment statistics. Pathways reaching a significance of FDR adjusted p < 0.05 are shown. Bubble color is proportional to the normalized enrichment score (NES) and size to the significance, as indicated in the x axis.
Figure 3
Figure 3
Modulation of immune networks and metabolic pathways in the lower respiratory tract of COVID-19 patients. (A) BTM-driven meta-analysis of bronchoalveolar lavage fluid transcriptomes (BALF) (RNA-seq datasets CRA002390 and HRA000143) from COVID-19 patients (7, 10). Gene lists were pre-ranked by log2 fold change of experimental samples over healthy controls and used as input in GSEA, with BTMs or KEGG metabolic pathways as gene sets, 1,000 permutations and weighted enrichment statistics. BTMs or metabolic pathways reaching a significance of nominal p < 0.05 and consistently regulated in both datasets are shown. BTMs are denoted by the black borders and metabolic pathways by gray borders. Bubble colors represent the normalized enrichment score (NES) regulation and sizes are proportional to the significance of the association. (B,C) Enrichment plots for the BTMs chemokines and inflammatory molecules in myeloid cells (M86.0) and enriched in neutrophils (M37.1) from an independent sample of one COVID-19 patient's lung tissue (RNA-seq dataset GSE147507) (9). The gene list was pre-ranked by log2 fold change of the experimental sample over healthy controls and used as input in GSEA with the BTMs as gene sets, 1,000 permutations and weighted enrichment statistics.
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