Exploring host cellular responses to SARS-CoV-2 variants using RNA sequencing of infected human airway epithelial cells

This study explores the interaction between various SARS-CoV-2 variants and host cells by using RNA sequencing (RNA-seq) of human airway epithelial cells. The primary objective was to elucidate the host cellular responses, including metabolic and immune pathways, to different SARS-CoV-2 variants. Human airway epithelial cells were infected with SARS-CoV-2 variants (IC19, Alpha, Beta, Delta, Omicron BA.1, and BA.5) and compared with mock samples at time 0. Samples were collected at 24 and 72 hours post-infection for RNA isolation and subsequent bulk RNA-sequencing. The RNA-seq data processing involved quality checks, mapping reads to a reference genome, and identifying differentially expressed genes to determine changes in gene expression in response to viral infection. This analysis aims to provide a deeper understanding of the molecular interactions between SARS-CoV-2 variants and host cells, contributing to the broader knowledge of viral pathogenicity and host-pathogen interactions. Overall design: In this study, we aimed to investigate variations in gene expression among different SARS-CoV-2 variants. To achieve this, primary human nasal epithelial cells (hNECs) from pools of human donors (Epithelix) were subjected to single infections with a multiplicity of infection (MOI) of 0.1 using SARS-CoV-2 variants (Alpha, Beta, Delta, Omicron BA.1, and BA.5). The control viral strain used was the lineage B.1 SARS-CoV-2/England/IC19/2020 (IC19) isolate (EPI_ISL_475572). Another group remained uninfected, all maintained at 37°C with 5% CO2. Control uninfected samples and cells stimulated with recombinant interferon alpha (IFNa; 1000 U/ml) for 24 hours were also included. Experiments were conducted with two different batches of human donor hNECs to account for variability. RNA samples were collected from uninfected, stimulated, and virus-infected hNECs at 24 and 72 hours post-infection to assess gene expression changes. The quality and integrity of RNA samples were evaluated using the Agilent 2100 Bioanalyzer (Agilent Technologies) before proceeding with library preparation. Strand-specific RNA sequencing was performed at Novogene (Cambridge, UK). Subsequently, FASTQ files were processed using Partek Flow for data analysis. Differentially expressed genes were identified by applying stringent criteria, including statistical significance (P < 0.05) and fold change (>2), using at least two replicates for each condition. By comparing the gene expression patterns, we aimed to uncover the pathways impacted by viral infections.