Single-cell analysis of lung epithelial cells reveals age and cell population-specific responses to SARS-CoV-2 infection in ciliated cells (preprint)

The ability of SARS-CoV-2 to evade antiviral immune signaling in the airway contributes to the severity of COVID-19 disease. Additionally, COVID-19 is influenced by age and has more severe presentations in older individuals. This raises questions about innate immune signaling as a function of lung development and age. Therefore, we investigated the transcriptome of different cell populations of the airway epithelium using pediatric and adult lung tissue samples from the LungMAP Human Tissue Core Biorepository. Specifically, lung lobes were digested and cultured into a biomimetic model of the airway epithelium on an air-liquid interface. Cells were then infected with SARS-CoV-2 and subjected to single-cell RNA sequencing. Transcriptional profiling and differential expression analysis were carried out using Seurat. The clustering analysis identified several cell populations: club cells, proliferating epithelial cells, multiciliated precursor cells, ionocytes, and two biologically distinct clusters of ciliated cells (FOXJ1high and FOXJ1low). Interestingly, the two ciliated cell clusters showed different infection rates and enrichment of processes involved in ciliary biogenesis and function; we observed a cell-type-specific suppression of innate immunity in infected cells from the FOXJ1low subset. We also identified a significant number of genes that were differentially expressed in lung cells derived from children as compared to adults, suggesting the differential pathogenesis of SARS-CoV-2 infection in children versus adults. We discuss how this work can be used to identify drug targets to modulate molecular signaling cascades that mediate an innate immune response and begin to understand differences in COVID-19 outcomes for pediatric vs. adult populations.

Generation and functional analysis of defective viral genomes during SARS-CoV-2 infection (preprint)

Defective viral genomes (DVGs) have been identified in many RNA viruses as a major factor influencing antiviral immune response and viral pathogenesis. However, the generation and function of DVGs in SARS-CoV-2 infection are less known. In this study, we elucidated DVG generation in SARS-CoV-2 and its relationship with host antiviral immune response. We observed DVGs ubiquitously from RNA-seq datasets of in vitro infections and autopsy lung tissues of COVID-19 patients. Four genomic hotspots were identified for DVG recombination and RNA secondary structures were suggested to mediate DVG formation. Functionally, bulk and single cell RNA-seq analysis indicated the IFN stimulation of SARS-CoV-2 DVGs. We further applied our criteria to the NGS dataset from a published cohort study and observed significantly higher DVG amount and frequency in symptomatic patients than that in asymptomatic patients. Finally, we observed unusually high DVG frequency in one immunosuppressive patient up to 140 days after admitted to hospital due to COVID-19, first-time suggesting an association between DVGs and persistent viral infections in SARS-CoV-2. Together, our findings strongly suggest a critical role of DVGs in modulating host IFN responses and symptom development, calling for further inquiry into the mechanisms of DVG generation and how DVGs modulate host responses and infection outcome during SARS-CoV-2 infection.

Gene Expression Risk Scores for COVID-19 Illness Severity (preprint)

BackgroundThe correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood. MethodsWe assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2-infection clinically adjudicated as having mild, moderate or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and non-severe COVID. ResultsGene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus non-severe illness, we identified >4000 genes differentially expressed (FDR<0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated ROC-AUC=0.98), and need for intensive care in an independent cohort (ROC-AUC=0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort. ConclusionThese data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.