Host, Viral, and Environmental Transcriptome Profiles of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

Daniel J Butler; Christopher Mozsary; Cem Meydan; David C Danko; Jonathan Foox; Joel Rosiene; Alon Shaiber; Ebrahim Afshinnekoo; Matthew MacKay; Fritz J Sedlazeck; Nikolay A Ivanov; Maria A Sierra; Diana Pohle; Michael Zeitz; Vijendra Ramlall; Undina Gisladottir; Craig D Westover; Krista Ryon; Benjamin Young; Chandrima Bhattacharya; Phyllis Ruggiero; Bradley W Langhorst; Nathan A Tanner; Justyn Gawrys; Dmitry Meleshko; Dong Xu; Jenny Xiang; Angelika Iftner; Daniela Bezdan; John Sipley; Lin Cong; Arryn Craney; Priya Velu; Ari Melnick; Iman A Hajirasouliha; Thomas Iftner; Mirella Salvatore; Massimo Loda; Lars F Westblade; Shawn Levy; Melissa Cushing; Nicholas P Tatonetti; Marcin Imielinski; Hanna Rennert; Christopher Mason

We leveraged the comprehensive nature of the total RNA-seq profiles to define the host transcriptome during SARS-CoV-2 infection. We first integrated the LAMP, qRT-PCR, and total RNA-Seq viral load estimates to identify a final set of 93 SARS-CoV-2 positive (COVID-19 positive) and 204 negative (COVID-19 negative) cases with coverage across the host and viral genomes. Differentially expressed genes (DEGs) associated with SARS-CoV-2 infection were calculated using DESeq2. (see Methods). Overall, 5,982 significant DEGs (q<0.01, >1.5-fold change) were found for the COVID-19 positive samples (Supp . Table 3 ) , spanning 2,942 upregulated DEGs and 3,040 down-regulated DEGs, which could be separated into high, medium, and low viral response categories based on the three detection methodologies (qRT-PCR, NGS, and LAMP) (Figure 6b ). To test for the possibility of cell proportion changes due to infection, we used Bisque to predict the cell types for all COVID samples, which showed consistent cell distributions of mostly goblet and ciliated airway cells for all samples (Supp. Figure 9 )