Communications BiologyBioinformaticsPeer-reviewed journal article
Genome-wide bioinformatic analyses predict key host and viral factors in SARS-CoV-2 pathogenesis
Mariana Galvão FerrariniiD, Avantika LaliD, Rita RebolloiD, Andreas J GruberiD, Andrea GuarracinoiD, Itziar Martínez-GonzáleziD, Taylor Floyd, Daniel Siqueira de OliveiraiD, Justin Shanklin, Ethan Beausoleil, Taneli PusaiD, Brett E PickettiD, Vanessa Aguiar‐PulidoiD
Abstract
The novel betacoronavirus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) caused a worldwide pandemic (COVID-19) after initially emerging in Wuhan, China. Here we applied a novel, comprehensive bioinformatic strategy to public RNA sequencing and viral genome sequencing data, to better understand how SARS-CoV-2 interacts with human cells. To our knowledge, this is the first meta-analysis to predict host factors that play a specific role in SARS-CoV-2 pathogenesis, distinct from other respiratory viruses. We identified differentially expressed genes, isoforms and transposable element families specifically altered in SARS-CoV-2 infected cells. Well-known immunoregulators including CSF2, IL-32, IL-6 and SERPINA3 were differentially expressed, while immunoregulatory transposable element families were overexpressed. We predicted conserved interactions between the SARS-CoV-2 genome and human RNA-binding proteins such as hnRNPA1, PABPC1 and eIF4b, which may play important roles in the viral life cycle. We also detected four viral sequence variants in the spike, polymerase, and nonstructural proteins that correlate with severity of COVID-19. The host factors we identified likely represent important mechanisms in the disease profile of this pathogen, and could be targeted by prophylactics and/or therapeutics against SARS-CoV-2. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/225581v3_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@15ca5a6org.highwire.dtl.DTLVardef@17f455forg.highwire.dtl.DTLVardef@a39f50org.highwire.dtl.DTLVardef@306ec1_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 48
- FWCI
- 3.72
- Field percentile
- top 5%
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