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Identification and classification of differentially expressed genes reveal potential molecular signature associated with SARS-CoV-2 infection in lung adenocarcinomal cells.

ABSTRACT: Genomic techniques such as next-generation sequencing and microarrays have facilitated the identification and classification of molecular signatures inherent in cells upon viral infection, for possible therapeutic targets. Therefore, in this study, we performed a differential gene expression analysis, pathway enrichment analysis, and gene ontology on RNAseq data obtained from SARS-CoV-2 infected A549 cells. Differential expression analysis revealed that 753 genes were up-regulated while 746 down-regulated. SNORA81, OAS2, SYCP2, LOC100506985, and SNORD35B are the top 5 upregulated genes upon SARS-Cov-2 infection. Expectedly, these genes have been implicated in the immune response to viral assaults. In the Ontology of protein classification, a high percentage of the genes are classified as Gene-specific transcriptional regulator, metabolite interconversion enzyme, and Protein modifying enzymes. Twenty pathways with P-value lower than 0.05 were enriched in the up-regulated genes while 18 pathways are enriched in the down-regulated DEGs. The toll-like receptor signalling pathway is one of the major pathways enriched. This pathway plays an important role in the innate immune system by identifying the pathogen-associated molecular signature emanating from various microorganisms. Taken together, our results present a novel understanding of genes and corresponding pathways upon SARS-Cov-2 infection, and could facilitate the identification of novel therapeutic targets and biomarkers in the treatment of COVID-19.

SUBMITTER: Soremekun OS

PROVIDER: S-EPMC7308782 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification and classification of differentially expressed genes reveal potential molecular signature associated with SARS-CoV-2 infection in lung adenocarcinomal cells.

Soremekun Opeyemi S OS Omolabi Kehinde F KF Soliman Mahmoud E S MES

Informatics in medicine unlocked 20200623

Genomic techniques such as next-generation sequencing and microarrays have facilitated the identification and classification of molecular signatures inherent in cells upon viral infection, for possible therapeutic targets. Therefore, in this study, we performed a differential gene expression analysis, pathway enrichment analysis, and gene ontology on RNAseq data obtained from SARS-CoV-2 infected A549 cells. Differential expression analysis revealed that 753 genes were up-regulated while 746 down ...[more]

PMID: 32835074

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Project description:BackgroundThe ongoing COVID-19 outbreak has caused devastating mortality and posed a significant threat to public health worldwide. Despite the severity of this illness and 2.3 million worldwide deaths, the disease mechanism is mostly unknown. Previous studies that characterized differential gene expression due to SARS-CoV-2 infection lacked robust validation. Although vaccines are now available, effective treatment options are still out of reach.ResultsTo characterize the transcriptional activity of SARS-CoV-2 infection, a gene signature consisting of 25 genes was generated using a publicly available RNA-Sequencing (RNA-Seq) dataset of cultured cells infected with SARS-CoV-2. The signature estimated infection level accurately in bronchoalveolar lavage fluid (BALF) cells and peripheral blood mononuclear cells (PBMCs) from healthy and infected patients (mean 0.001 vs. 0.958; P < 0.0001). These signature genes were investigated in their ability to distinguish the severity of SARS-CoV-2 infection in a single-cell RNA-Sequencing dataset. TNFAIP3, PPP1R15A, NFKBIA, and IFIT2 had shown bimodal gene expression in various immune cells from severely infected patients compared to healthy or moderate infection cases. Finally, this signature was assessed using the publicly available ConnectivityMap database to identify potential disease mechanisms and drug repurposing candidates. Pharmacological classes of tricyclic antidepressants, SRC-inhibitors, HDAC inhibitors, MEK inhibitors, and drugs such as atorvastatin, ibuprofen, and ketoconazole showed strong negative associations (connectivity score < - 90), highlighting the need for further evaluation of these candidates for their efficacy in treating SARS-CoV-2 infection.ConclusionsThus, using the 25-gene SARS-CoV-2 infection signature, the SARS-CoV-2 infection status was captured in BALF cells, PBMCs and postmortem lung biopsies. In addition, candidate SARS-CoV-2 therapies with known safety profiles were identified. The signature genes could potentially also be used to characterize the COVID-19 disease severity in patients' expression profiles of BALF cells.

Project description:SARS-CoV and SARS-CoV-2 encode spike proteins that bind human ACE2 on the cell surface to enter target cells during infection. A small fraction of humans encode variants of ACE2, thus altering the biochemical properties at the protein interaction interface. These and other ACE2 coding mutants can reveal how the spike proteins of each virus may differentially engage the ACE2 protein surface during infection. We created an engineered HEK 293T cell line for facile stable transgenic modification, and expressed the major human ACE2 allele or 28 of its missense mutants, 24 of which are possible through single nucleotide changes from the human reference sequence. Infection with SARS-CoV or SARS-CoV-2 spike pseudotyped lentiviruses revealed that high ACE2 cell-surface expression could mask the effects of impaired binding during infection. Drastically reducing ACE2 cell surface expression revealed a range of infection efficiencies across the panel of mutants. Our infection results revealed a non-linear relationship between soluble SARS-CoV-2 RBD binding to ACE2 and pseudovirus infection, supporting a major role for binding avidity during entry. While ACE2 mutants D355N, R357A, and R357T abrogated entry by both SARS-CoV and SARS-CoV-2 spike proteins, the Y41A mutant inhibited SARS-CoV entry much more than SARS-CoV-2, suggesting differential utilization of the ACE2 side-chains within the largely overlapping interaction surfaces utilized by the two CoV spike proteins. These effects correlated well with cytopathic effects observed during SARS-CoV-2 replication in ACE2-mutant cells. The panel of ACE2 mutants also revealed altered ACE2 surface dependencies by the N501Y spike variant, including a near-complete utilization of the K353D ACE2 variant, despite decreased infection mediated by the parental SARS-CoV-2 spike. Our results clarify the relationship between ACE2 abundance, binding, and infection, for various SARS-like coronavirus spike proteins and their mutants, and inform our understanding for how changes to ACE2 sequence may correspond with different susceptibilities to infection.

Dataset Information

Identification and classification of differentially expressed genes reveal potential molecular signature associated with SARS-CoV-2 infection in lung adenocarcinomal cells.

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Identification and classification of differentially expressed genes reveal potential molecular signature associated with SARS-CoV-2 infection in lung adenocarcinomal cells.

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