CompCorona: A web application for comparative transcriptome analyses of coronaviruses reveals SARS-CoV-2-specific host response

Abstract

Background/aim: Understanding the mechanism of host transcriptomic response to infection by the SARS-CoV-2 virus is crucial, especially for patients suffering from long-term effects of COVID-19, such as long COVID or pericarditis inflammation, potentially linked to side effects of the SARS-CoV-2 spike proteins. We conducted comprehensive transcriptome and enrichment analyses on lung and peripheral blood mononuclear cells (PBMCs) infected with SARS-CoV-2, as well as on SARS-CoV and MERS-CoV, to uncover shared pathways and elucidate their common disease progression and viral replication mechanisms.

Materials and methods: We developed CompCorona, the first interactive online tool for visualizing gene response variance among the family Coronaviridae through 2D and 3D principal component analysis (PCA) and exploring systems biology variance using pathway plots. We also made preprocessed datasets of lungs and PBMCs infected by SARS-CoV-2, SARS-CoV, and MERS-CoV publicly available through CompCorona.

Results: One remarkable finding from the lung and PBMC datasets for infections by SARS-CoV-2, but not infections by other coronaviruses (CoVs), was the significant downregulation of the angiogenin (ANG) and vascular endothelial growth factor A (VEGFA) genes, both directly involved in epithelial and vascular endothelial cell dysfunction. Suppression of the TNF signaling pathway was also observed in cells infected by SARS-CoV-2, along with simultaneous activation of complement and coagulation cascades and pertussis pathways. The ribosome pathway was found to be universally suppressed across all three viruses. The CompCorona online tool enabled the comparative analysis of 9 preprocessed host transcriptome datasets of cells infected by CoVs, revealing the specific host response differences in cases of SARS-CoV-2 infection. This included identifying markers of epithelial dysfunction via interactive 2D and 3D PCA, Venn diagrams, and pathway plots.

Conclusion: Our findings suggest that infection by SARS-CoV-2 might induce pulmonary epithelial dysfunction, a phenomenon not observed in cells infected by other CoVs. The publicly available CompCorona tool, along with the preprocessed datasets of cells infected by various CoVs, constitutes a valuable resource for further research into CoV-associated syndromes.

Keywords: Middle East respiratory syndrome coronavirus; SARS-CoV-2; epithelial dysfunction; principal component analysis; severe acute respiratory syndrome-related coronavirus; web portal.

Figure 1

Workflow of RNA-seq, differential expression gene (DEG), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The figure illustrates the stepwise process employed in the study, encompassing raw RNA-seq data acquisition, quality control, preprocessing, alignment to a reference genome, identification of DEGs, and subsequent GO and KEGG enrichment analyses.

Figure 2

Lung SARS-CoV-2 protein–protein interactions (PPIs). A) Visualization of 2172 PPIs derived from StringDB, associated with 615 differentially expressed genes, using Cytoscape. B) Exploration of PPI network modules using the ClusterViz app with Molecular Complex Detection (MCODE), a Cytoscape plug-in. An MCODE score greater than 2 was set as the cut-off criterion, with default parameters including a degree cut-off of 2, node score cut-off of 0.2, K-core of 3, and max depth of 100. C) Identification of hub genes using cytoHubba plug-in within the PPI network module. The maximal clique centrality method was employed, revealing 20 hub genes. Coloring in the figure corresponds to the rank score, with the highest score represented in dark red.

Figure 3

A) Violin plot illustrating the fold change (fc) values of differentially expressed genes obtained from SARS-CoV-2, MERS, and SARS infections. B) Bar plot depicting the numbers of downregulated and upregulated differentially expressed genes in SARS-CoV-2, MERS, and SARS infections.

Figure 4

A) Upset plot: Visual representation of gene set intersections in SARS-CoV-2, SARS-CoV, and MERS-CoV infections, offering a comprehensive view of shared and unique genes. B) Venn diagram: Illustration of common and distinct genes among SARS-CoV-2, SARS-CoV, and MERS-CoV infections, highlighting shared and unique genetic components. C) Heatmaps: Detailed gene expression patterns within the intersections of SARS-CoV-2, SARS-CoV, and MERS-CoV infections. Red hues denote high log²FC values (upregulation), while blue indicates low values (downregulation).

Figure 5

CompCorona web interface overview. A) The CompCorona web structure presents users with a comprehensive interface featuring a Venn diagram, 2D and 3D principal component analysis (PCA), and an interactive section for uploading differentially expressed gene (DEG) data. PCA results are illustrated as an example. PCA reveals significant variance in gene expression patterns, with Dim1 and Dim2 capturing 27.5% and 25.2% of the total variability, respectively. B) A Venn diagram is presented in a clickable format, showcasing a comparative analysis involving SARS-CoV-2, MERS-CoV, and blood-based SARS-CoV-2. Additionally, a pathway analysis result for SARS-CoV-2 DEG data is displayed, demonstrating the versatility of the platform.