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. 2020 Jul 7;11(7):760.
doi: 10.3390/genes11070760.

Protein Coding and Long Noncoding RNA (lncRNA) Transcriptional Landscape in SARS-CoV-2 Infected Bronchial Epithelial Cells Highlight a Role for Interferon and Inflammatory Response

Radhakrishnan Vishnubalaji  1 , Hibah Shaath  1   2 , Nehad M Alajez  1   2
Affiliations

Protein Coding and Long Noncoding RNA (lncRNA) Transcriptional Landscape in SARS-CoV-2 Infected Bronchial Epithelial Cells Highlight a Role for Interferon and Inflammatory Response

Radhakrishnan Vishnubalaji et al. Genes (Basel). .

Abstract

The global spread of COVID-19, caused by pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) underscores the need for an imminent response from medical research communities to better understand this rapidly spreading infection. Employing multiple bioinformatics and computational pipelines on transcriptome data from primary normal human bronchial epithelial cells (NHBE) during SARS-CoV-2 infection revealed activation of several mechanistic networks, including those involved in immunoglobulin G (IgG) and interferon lambda (IFNL) in host cells. Induction of acute inflammatory response and activation of tumor necrosis factor (TNF) was prominent in SARS-CoV-2 infected NHBE cells. Additionally, disease and functional analysis employing ingenuity pathway analysis (IPA) revealed activation of functional categories related to cell death, while those associated with viral infection and replication were suppressed. Several interferon (IFN) responsive gene targets (IRF9, IFIT1, IFIT2, IFIT3, IFITM1, MX1, OAS2, OAS3, IFI44 and IFI44L) were highly upregulated in SARS-CoV-2 infected NBHE cell, implying activation of antiviral IFN innate response. Gene ontology and functional annotation of differently expressed genes in patient lung tissues with COVID-19 revealed activation of antiviral response as the hallmark. Mechanistic network analysis in IPA identified 14 common activated, and 9 common suppressed networks in patient tissue, as well as in the NHBE cell model, suggesting a plausible role for these upstream regulator networks in the pathogenesis of COVID-19. Our data revealed expression of several viral proteins in vitro and in patient-derived tissue, while several host-derived long noncoding RNAs (lncRNAs) were identified. Our data highlights activation of IFN response as the main hallmark associated with SARS-CoV-2 infection in vitro and in human, and identified several differentially expressed lncRNAs during the course of infection, which could serve as disease biomarkers, while their precise role in the host response to SARS-CoV-2 remains to be investigated.

Keywords: COVID-19; IFN response; MAPK; SARS-CoV-2; bronchial epithelial; gene expressions; immune response; lncRNAs; pathway analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Clustering of SARS-CoV-2 and control NBHE cells based on mRNA RNA-seq analysis. (a) Hierarchical clustering of control and SARS-CoV-2 infected NHBE cells based on differentially expressed mRNAs. Each column represents one replica, while each row represents one mRNA. Expression is depicted at the indicated color scales. (b) Volcano plot representation of significantly altered genes in NHBE SARS-CoV-2 vs. mock infected cells. Red and blue colors indicate the genes with significantly increased or decreased expression, respectively. (c) Relative expression of the indicated viral genes in SARS-COV-2 NHBE vs. control NHBE cells. (d) Marker discovery analysis to identify putative markers that are selectively expressed in control vs. SARS-CoV-2 infected NBHE cells. Enriched gene ontology (GO) associations are indicated on the y axis.
Figure 2
Figure 2
Ingenuity pathway analysis (IPA) of differentially expressed gene in Control and SARS-CoV-2 infected NBHE cells. (a) Canonical IPA analysis of differentially expressed genes in SARS-CoV-2 vs. control NBHE cells. X- axis indicates Z score while y-axis indicates the corresponding canonical pathways. Blue color indicates suppressed pathways. (b) Upstream regulator analysis of differentially expressed genes in SARS-CoV-2 vs. control NBHE cells using IPA. IgG (c) and interferon lambda 1 (IFNL1, (d)) mechanistic network and their activation state in SARS-CoV-2 infected NBHE cells based on IPA analysis.
Figure 3
Figure 3
Downstream effector analysis of differentially expressed genes in SARS-CoV-2 infected NBHE cells. (a) Tree map (hierarchical heat map) depicting affected functional categories based on differentially expressed genes where the major boxes represent a category of diseases and functions. Each individual colored rectangle is a particular biological function or disease and the color range indicates its predicted activation state—increasing (orange) or decreasing (blue). Darker colors indicate higher absolute Z-scores. In this default view, the size of the rectangles is correlated with increasing overlap significance. (b) Bar graph depicting the activated (red) and suppressed (blue) functional categories. (c) Expression of selected gene from the antiviral defense genes category in mock and SARS-COV-2 infected NHBE cells.
Figure 4
Figure 4
Clustering of SARS-CoV-2 and control NBHE cells based on lncRNA expression. (a) Hierarchical clustering of control and SARS-CoV-2 infected NHBE cells based on differentially expressed lncRNAs. Each column represents one replica, while each row represents one lncRNA. Expression is depicted at the indicated color scale. (b) Volcano plot representation of differential expression analysis of lncRNAs in NHBE SARS-CoV-2 vs. mock infected cells. Red and blue colors indicate the genes with significantly increased or decreased expression, respectively. (c) Expression of selected lncRNAs in mock and SARS-COV-2 infected NHBE cells.
Figure 5
Figure 5
Alteration in mRNA and lncRNA expression in lung biopsies from patient with COVID-19. (a) Marker discovery analysis to identify putative markers that are selectively expressed in the lungs from COVID-19 subjects compared to normal lung tissue. Enriched gene ontology (GO) associations are indicated on the y axis (left) while gene guides are listed on the right side. (b) Relative expression of the indicated viral genes in lung tissue from COVID-19 patient. Comparative analysis of differentially expressed mRNAs (c) and lncRNAs (d) in NHBE compared to patient-derived lung tissue.
Figure 6
Figure 6
Mechanistic Network Analysis Predicts central role for IFNG in the host response of patients with COVID-19 infection. (a) Top ten activated and top ten inhibited upstream regulator networks in lung tissue derived from COVID-19 patients based on transcriptome and IPA analyses. (b) Venn diagram illustrating the overlap between activated and suppressed upstream regulator networks in SARS-COV-2 NHBE and lung tissue from COVID-19 patients based on RNA-seq and IPA analysis. (c) Illustration of the IFNG mechanistic network according to subcellular localization. Activation state is depicted according to color scale.
Figure 7
Figure 7
Suppression of viral infection and replication based on transcriptome and IPA analysis of lung-derived tissue from COVID-19 patient. Regulator effects network analysis based on IPA revealed suppression of viral infection and replication in lung tissue from patients with SASRS-COV-2 infection. Network highlights a role for BTK, EIF2AK2, IFNA2, IFNG, IFNL1, IL1RN, Interferon α, IRF9, JAK, MAPK1, PAF1, PRL, RNY3, SGPL1, SPI1, STAT1, TGM2 and USP18 in mediating these inhibitory effects as illustrated. Activation state is depicted according to the color scale.
Figure 8
Figure 8
Illustration of the viral infection functional category according to subcellular localization based on transcriptome and IPA analysis of lung-derived tissue from COVID-19 patients.
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