Impact on splicing in Saccharomyces cerevisiae of random 50-base sequences inserted into an intron

  1. Josh T. Cuperus1
  1. 1Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
  2. 2Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, USA
  3. 3Department of Medicine, University of Washington, Seattle, Washington 98195, USA
  1. Corresponding authors: fields{at}uw.edu, cuperusj{at}uw.edu
  1. Handling editor: Benjamin Blencowe

Abstract

Intron splicing is a key regulatory step in gene expression in eukaryotes. Three sequence elements required for splicing—5′ and 3′ splice sites and a branchpoint—are especially well-characterized in Saccharomyces cerevisiae, but our understanding of additional intron features that impact splicing in this organism is incomplete, due largely to its small number of introns. To overcome this limitation, we constructed a library in S. cerevisiae of random 50-nt (N50) elements individually inserted into the intron of a reporter gene and quantified canonical splicing and the use of cryptic splice sites by sequencing analysis. More than 70% of approximately 140,000 N50 elements reduced splicing by at least 20%. N50 features, including higher GC content, presence of GU repeats, and stronger predicted secondary structure of its pre-mRNA, correlated with reduced splicing efficiency. A likely basis for the reduced splicing of such a large proportion of variants is the formation of RNA structures that pair N50 bases—such as the GU repeats—with other bases specifically within the reporter pre-mRNA analyzed. However, multiple models were unable to explain more than a small fraction of the variance in splicing efficiency across the library, suggesting that complex nonlinear interactions in RNA structures are not accurately captured by RNA structure prediction methods. Our results imply that the specific context of a pre-mRNA may determine the bases allowable in an intron to prevent secondary structures that reduce splicing. This large data set can serve as a resource for further exploration of splicing mechanisms.

Keywords

  • Received June 21, 2023.
  • Accepted October 18, 2023.

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