Analyzing transcriptomic landscape of SARS-CoV-2-infected lung cells

In a recent study posted to the bioRxiv* pre-print server, researchers using deep transcriptome analysis examined the coding and non-coding transcriptional landscape of lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Study: Transcriptomic landscapes of SARS-CoV-2-infected and bystander lung cells reveal a selective upregulation of NF-κB-dependent coding and non-coding proviral transcripts. Image Credit: Kris Petkong/Shutterstock
Study: Transcriptomic landscapes of SARS-CoV-2-infected and bystander lung cells reveal a selective upregulation of NF-κB-dependent coding and non-coding proviral transcripts. Image Credit: Kris Petkong/Shutterstock

Multiple previous studies have focused on genome-wide investigations of host cellular responses to SARS-CoV-2 infection using bulk ribonucleic acid (RNA)-sequencing techniques in mixed populations of infected and uninfected cells.

Bulk transcriptome studies are inefficient in portraying the variation of the host transcripts. Moreover, the noise, technical variability, and massive sample size of small conditional RNA (scRNA)- sequencing data raised challenges in analyzing the total number of differentially expressed genes (DEGs).

More importantly, most bulk and single-cell transcriptomic studies have evaluated the expression of the annotated messenger RNAs (mRNAs). However, unannotated RNAs and other transcripts, including long non-coding RNAs (lncRNAs) remain unexamined. The lncRNAs, which are at least 200 nucleotides long, play fundamental roles in cellular identity, development, and disease progression through epigenetic or post-transcriptional regulation of mRNA expression. Reports suggest that over 58,000 lncRNA loci are there in the human genome, a majority of which could be 5’-capped, polyadenylated (poly A+).

Increasing evidence suggests the involvement of lncRNAs in virus-host interactions and antiviral immunity. Although efforts are in progress to uncover the unmapped unannotated RNAs using both reference-based and unreferenced methods, so far, none of these strategies have been able to decipher virus-cell interactions.

About the study

In the present study, researchers used A549 cells (human alveolar basal epithelial carcinoma cells), stably expressing angiotensin-converting enzyme 2 (ACE2), infected with SARS-CoV-2 at a multiplicity of infection (MOI) of one for 24 hours to examine transcriptomic changes in SARS-CoV-2 infected cells.

The A549 cells were then fixed, stained intracellularly using anti-spike (S) antibodies, and sorted into S-positive (infected cells, S+) and S-negative (bystander, S-) populations. Around 15% of A549-ACE2 cells were positive for S protein. S- cells represented either uninfected cells or cells at an early stage of infection before viral protein production. Mock-infected cells served as negative controls. Using principal component analysis (PCA), they segregated polyA+ RNAs/transcriptomes into S+ cells from S- and the mock-infected cells.

They used gencode annotation to identify coding and long non-coding genes, whereas Scallop assembler helped examine unannotated RNAs. Using gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, a knowledge base for linking genomes to high-level biological functions, they analyzed pathways affected by SARS-CoV-2 infection in A549-ACE2 cells.


Since ~85% of the total reads mapped to the SARS-CoV-2 genome in S+ cells and less than 5% aligned with the viral genome in S- cells, this validated the sorting approach used in the study. The dominance of SARS-CoV-2 reads over cellular reads and illustrated the ability of SARS-CoV-2 to hijack the cellular machinery for its replication. Further, there are 13-times more downregulated genes in S+ cells, indicating that SARS-CoV-2 infection triggers a massive but partial shutdown of host cell gene expression.

The transcriptional landscapes of bystander and mock-infected cell populations were very similar. In S+ cells, around 1,260 and 184 genes from annotated lncRNA were down- and up-regulated, respectively. RFPL3S, ADIRF-AS1, and WAKMAR2 were among the top 15 up-regulated lncRNAs in S+ cells.

While the functions of RFPL3S and ADIRF-AS1 are not known, WAKMAR2 restricted nuclear factor kappa B (NF-kB) induced the production of inflammatory chemokines in human keratinocytes. Among the top downregulated lncRNAs, two genes, including HOXA-AS2 and NKILA, have shown negative regulation of NF-kB signaling in endothelial cells and breast cancer cell lines, respectively. These findings suggested that altered expression of WAKMAR2, HOXA-AS2, and NKILA in S+ cells could play a role in viral-associated inflammation.

From a total of 1,400 unannotated transcripts, Scallop assembler detected around 800 unannotated polyA+ RNA transcripts that expressed differently in S+ cells.

Gene ontology (GO) and KEGG pathway analyses confirmed the upregulation of tumor necrosis factor (TNF) and NF-κB- transcriptional signatures in S+ cells. Among the 741 upregulated protein-coding genes identified in S+ cells, 68 possessed an NF-κB binding site in their promoter regions. In vivo studies have demonstrated that NF-κB function is crucial for mounting an antiviral response; thus, many viruses have evolved strategies to counteract the NF-κB-mediated antiviral response.        

The current study data showed that the disruption of NF-κB function through silencing of subunits p105/p50 diminished the production of viral RNAs and proteins at 24 hpi in A549-ACE2 cells, confirming its proviral role. Similarly, Nsp5 also induced the expression of several inflammatory cytokines, such as interleukin-6 (IL-6) and TNF-α, through activation of NF-κB in Calu-3 cells. Yet, further studies are required to understand how SARS-CoV-2 benefits from hijacking NF-κB-driven functions.

Numerous genes associated with the NF-κB signaling pathway fall into the category of genes that escape the virus-induced cellular shutoff. Among the 68 upregulated NF-κB-targets in S+ cells, there were cytokines, such as CXCL8/IL8; these showed proviral functions via inhibition of the antiviral action of interferon-alpha (IFN-α) in the context of infection by several unrelated RNA and deoxyribonucleic acid (DNA) viruses. They may act similarly in SARS-CoV-2 infected A549-ACE2 cells.

ADIRF-AS1, an antisense lncRNA with no known function, was identified by the authors while analyzing NF-κB chromatin immunoprecipitation (ChIP)-sequencing data generated in A549 cells stimulated with TNF-α. They also recovered novel NF-κB target genes among unannotated genes.

Using small interfering RNA (siRNA)-mediated knock-down approaches, they explored the potential ability of ADIRF-AS1 to modulate the replication of SARS-CoV-2. They quantified intracellular viral RNA production by quantitative reverse transcriptase-polymerase chain reaction (RT-qPCR) 24 hours post-infection and used flow cytometric analysis to show the number of cells positive for the S protein. RT-qPCR analyses revealed that the reduced expression of ADIRF-AS1 significantly decreased the viral RNA yield, and the number of infected cells confirming ADIRF-AS1 also exhibited significant proviral functions.


Overall, the sorting approaches used in the study identified both coding and non-coding genes that contributed to optimal SARS-CoV-2 replication.

According to the authors, no previous study has identified a lncRNA with a direct action on the life cycle of SARS-CoV-2. They identified: i) lncRNA ADIRF-AS1, which was among the top upregulated lncRNAs in S+ cells; ii) an NF-κB binding site near its promoter region. Although a higher proportion of down- vs. up-regulated lncRNAs in S+ cells were identified, GO cannot be extrapolated from lncRNAs since most of them have no known function, indicating the need for future studies in this area.

Studies have identified hundreds of lncRNAs and analysis of a few of these has provided a much-needed glimpse of the potential regulatory impact of this class of RNAs on the IFN response and interferon-stimulated gene (ISG) expression. However, future studies should investigate their precise role in IFN-mediated antiviral response.

*Important notice

bioRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:
Neha Mathur

Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.


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