Comparison of Intracellular Transcriptional Response of NHBE Cells to Infection with SARS-CoV-2 Washington and New York Strains.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan, China in December 2019 and caused a global pandemic resulting in millions of deaths and tens of millions of patients positive tests. While studies have shown a D614G mutation in the viral spike protein are more transmissible, the effects of this and other mutations on the host response, especially at the cellular level, are yet to be fully elucidated. In this experiment we infected normal human bronchial epithelial (NHBE) cells with the Washington (D614) strain or the New York (G614) strains of SARS-CoV-2. We generated RNA sequencing data at 6, 12, and 24 hours post-infection (hpi) to improve our understanding of how the intracellular host response differs between infections with these two strains. We analyzed these data with a bioinformatics pipeline that identifies differentially expressed genes (DEGs), enriched Gene Ontology (GO) terms and dysregulated signaling pathways. We detected over 2,000 DEGs, over 600 GO terms, and 29 affected pathways between the two infections. Many of these entities play a role in immune signaling and response. A comparison between strains and time points showed a higher similarity between matched time points than across different time points with the same strain in DEGs and affected pathways, but found more similarity between strains across different time points when looking at GO terms. A comparison of the affected pathways showed that the 24hpi samples of the New York strain were more similar to the 12hpi samples of the Washington strain, with a large number of pathways related to translation being inhibited in both strains. These results suggest that the various mutations contained in the genome of these two viral isolates may cause distinct effects on the host transcriptional response in infected host cells, especially relating to how quickly translation is dysregulated after infection. This comparison of the intracellular host response to infection with these two SARS-CoV-2 isolates suggest that some of the mechanisms associated with more severe disease from these viruses could include virus replication, metal ion usage, host translation shutoff, host transcript stability, and immune inhibition.

Preprocessing of Public RNA-Sequencing Datasets to Facilitate Downstream Analyses of Human Diseases

Publicly available RNA-sequencing (RNA-seq) data are a rich resource for elucidating the mechanisms of human disease;however, preprocessing these data requires considerable bioinformatic expertise and computational infrastructure. Analyzing multiple datasets with a consistent computational workflow increases the accuracy of downstream meta-analyses. This collection of datasets represents the human intracellular transcriptional response to disorders and diseases such as acute lymphoblastic leukemia (ALL), B-cell lymphomas, chronic obstructive pulmonary disease (COPD), colorectal cancer, lupus erythematosus;as well as infection with pathogens including Borrelia burgdorferi, hantavirus, influenza A virus, Middle East respiratory syndrome coronavirus (MERS-CoV), Streptococcus pneumoniae, respiratory syncytial virus (RSV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We calculated the statistically significant differentially expressed genes and Gene Ontology terms for all datasets. In addition, a subset of the datasets also includes results from splice variant analyses, intracellular signaling pathway enrichments as well as read mapping and quantification. All analyses were performed using well-established algorithms and are provided to facilitate future data mining activities, wet lab studies, and to accelerate collaboration and discovery.