GenomicsGenomicsPeer-reviewed journal article
Betacoronavirus-specific alternate splicing
Guy KarlebachiD, Bruce J. AronowiD, Stephen B. BayliniD, Daniel ButleriD, Jonathan FooxiD, Shawn A. Levy, Cem MeydaniD, Christopher MozsaryiD, Amanda Saravia-ButleriD, Deanne M. TayloriD, Eve Syrkin WurteleiD, Christopher E. MasoniD, Afshin BeheshtiiD, Peter N. RobinsoniD
Abstract
Viruses can subvert a number of cellular processes in order to block innate antiviral responses, and many viruses interact with cellular splicing machinery. SARS-CoV-2 infection was shown to suppress global mRNA splicing, and at least 10 SARS-CoV-2 proteins bind specifically to one or more human RNAs. Here, we investigate 17 published experimental and clinical datasets related to SARS-CoV-2 infection as well as datasets from the betacoronaviruses SARS-CoV and MERS as well as Streptococcus pneumonia, HCV, Zika virus, Dengue virus, influenza H3N2, and RSV. We show that genes showing differential alternative splicing in SARS-CoV-2 have a similar functional profile to those of SARS-CoV and MERS and affect a diverse set of genes and biological functions, including many closely related to virus biology. Additionally, the differentially spliced transcripts of cells infected by coronaviruses were more likely to undergo intron-retention, contain a pseudouridine modification and a smaller number of exons than differentially spliced transcripts in the control groups. Viral load in clinical COVID-19 samples was correlated with isoform distribution of differentially spliced genes. A significantly higher number of ribosomal genes are affected by DAS and DGE in betacoronavirus samples, and the betacoronavirus differentially spliced genes are depleted for binding sites of RNA-binding proteins. Our results demonstrate characteristic patterns of differential splicing in cells infected by SARS-CoV-2, SARS-CoV, and MERS, potentially modifying a broad range of cellular functions and affecting a diverse set of genes and biological functions.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: benchmarking
- research focus: database / resource
- approach: statistical model
- Citations
- 19
- FWCI
- 1.55
- Field percentile
- top 18%
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