Differential Expression of Viral Transcripts From Single-Cell RNA Sequencing of Moderate and Severe COVID-19 Patients and Its Implications for Case Severity - Liu, Teng; Jia, Peilin; Fang, Bingliang; Zhao, Zhongming (Frontiers in Microbiology, 2020)
With steady increase of new COVID-19 cases around the world, especially in the United States, health care resources in areas with the disease outbreak are quickly exhausted by overwhelming numbers of COVID-19 patients. Therefore, strategies that can effectively and quickly predict the disease progression and stratify patients for appropriate health care arrangements are urgently needed. We explored the features and evolutionary difference of viral gene expression in the SARS-CoV-2 infected cells from the bronchoalveolar lavage fluids of patients with moderate and severe COVID-19 using both single cell and bulk tissue transcriptome data. We found SARS-CoV-2 sequences were detectable in 8 types of immune related cells, including macrophages, T cells, and NK cells. We first reported that the SARS-CoV-2 ORF10 gene was differentially expressed in the severe versus moderate samples. Specifically, ORF10 was abundantly expressed in infected cells of severe cases, while it was barely detectable in the infected cells of moderate cases. Consequently, the expression ratio of ORF10 to nucleocapsid (N) was significantly higher in severe than moderate cases (p = 0.0062). Moreover, we found transcription regulatory sequences (TRSs) of the viral leader sequence-independent fusions with a 5’ joint point at position 1073 of SARS-CoV-2 genome were detected mainly in the patients with death outcome, suggesting its potential indication of clinical outcome. Finally, we identified the motifs in TRS of the viral leader sequence-dependent fusion events of SARS-CoV-2 and compared with that in SARS-CoV, suggesting its evolutionary trajectory. These results implicated potential roles and predictive features of viral transcripts in the pathogenesis of COVID-19 moderate and severe patients. Such features and evolutionary patterns require more data to validate in future.