Gene expression profiling of SARS-CoV-2 infections reveal distinct primary lung cell and systemic immune infection responses that identify pathways relevant in COVID-19 disease

Abstract

To identify key gene expression pathways altered with infection of the novel coronavirus SARS-CoV-2, we performed the largest comparative genomic and transcriptomic analysis to date. We compared the novel pandemic coronavirus SARS-CoV-2 with SARS-CoV and MERS-CoV, as well as influenza A strains H1N1, H3N2 and H5N1. Phylogenetic analysis confirms that SARS-CoV-2 is closely related to SARS-CoV at the level of the viral genome. RNAseq analyses demonstrate that human lung epithelial cell responses to SARS-CoV-2 infection are distinct. Extensive Gene Expression Omnibus literature screening and drug predictive analyses show that SARS-CoV-2 infection response pathways are closely related to those of SARS-CoV and respiratory syncytial virus infections. We validated SARS-CoV-2 infection response genes as disease-associated using Kaplan-Meier survival estimates in lung disease patient data. We also analysed COVID-19 patient peripheral blood samples, which identified signalling pathway concordance between the primary lung cell and blood cell infection responses.

Keywords: COVID-19; MERS-CoV; RNAseq; SARS-CoV; SARS-CoV-2; coronaviruses; transcriptomics.

Graphical Abstract

Figure 1

Phylogenetic and RNAseq analyses show the genetic relationship between the novel coronavirus SARS-CoV-2, and SARS, MERS and influenza strains. (A) Phylogenetic viral genome analysis shows SARS-CoV-2 is most closely related to SARS and MERS and is distinct from influenza A strains H1N1, H3N2 and H5N1. (B and C) Comparison of RNAseq analyses of infected primary human lung epithelial cells with these coronaviruses and influenza A strains indicates the common and distinct gene sets with upregulated expression in response to infection.

Figure 2

SARS-CoV-2 infection is associated with upregulation of a unique set of genes not seen with SARS, MERS or influenza A infections in human lung epithelial cells. (A) Heat map depicting the top 108 significant DEGs with infection of the novel coronavirus SARS-CoV-2, compared with top genes related to SARS, MERS and influenza infections. (B) An expanded view of the top 40 genes with increased expression in SARS-CoV-2 infection, indicating a unique expression profile not seen with the other viral infections. (C) Volcano plot highlights the most significant SARS-CoV-2 response genes above a log fold-change of 2 and adjusted P value <0.05. We see that although SARS-CoV-2 is closely related to SARS and MERS by viral phylogeny, the response of cells to infection is significantly different, thus representing another important and novel aspect of this virus.

Figure 3

Gene ontology and cell signalling pathway analysis finds enriched inflammatory and infection responses to SARS-CoV-2 infection. (A) Gene ontology analysis finds inflammatory and infection response (bacterial and viral responses pathways combined) significantly enriched with SARS-CoV-2 infection in human lung epithelial cells. (B) Cell signalling pathway analyses similarly find enrichment, predominantly inflammatory related signalling effects seen with SARS-CoV-2 infection. Analyses performed using The Gene Ontology, WikiPathways, BioCarta, Reactome and Panther databases.

Figure 4

Viral infection literature and drug prediction analysis validates SARS-CoV-2 infection responses and suggests relevant pathways for subsequent study. (A) A validation GEO viral literature screen was conducted of all publically available literature on viral infections. Analysis finds SARS-CoV-2 infection is most closely related to published studies of SARS and respiratory viral infections. (B) Predictive drug screening suggests molecules of interest targeting inflammatory, infection, lung fibrotic and coagulation factors may be of particular relevance to treating early-stage COVID-19 disease.

Figure 5

Kaplan–Meier survival estimates using lung adenocarcinoma datasets finds a significant relationship between SARS-CoV-2 infection response genes and patient survival. Kaplan–Meier estimates for the SARS-CoV-2 infection response genes BCL2A1, CSF2, EPSTI1, MMP13, CXCL6, OAS2, CXCL1, CXCL2, CXCL3, IFI6, IFI27 and TNF. These data indicate a significant relationship with increased mortality in LC patients, suggesting these genes as relevant co-morbidity factors in COVID-19 disease.

Figure 6

Targeted immune profiling of SARS-CoV-2 infected patient blood samples reveals systemic immune responses to infection, and perturbed cell signalling pathways that overlap with primary lung infection effects. (A) Heat map of targeted immune RNA profile of 32 patients (n = 10 healthy controls, n = 23 SARS-CoV-2 infected patients) using a NanoString-targeted immunology panel shows SARS-CoV-2 infected patients have a distinct systemic infection response. (B) A pooled analysis of time-course data reveals top gene dysregulated in patient blood by SARS-CoV-2 infection. (C) Only three genes are seen to be overlapping between primary lung cell and blood cell infection responses. (D) Nevertheless, we see concordance at the level of perturbed cell signalling pathways related to immune-inflammatory cytokine and interferon responses in the host.