bioRxivGeneticsPreprint
The strength of a gene's first 5' splice site is a key determinant of its expression
Yuta SakaiiD, Michael P. McGurkiD, Emma J. K. KowaliD, Elaine HuangiD, Christopher B. BurgeiD
Abstract
The control of gene expression is central to cellular physiology and to various biotechnological applications. In higher eukaryotes, synthesis of most messenger RNAs involves removal of introns during splicing. Intron-containing genes are often more highly expressed than intronless versions, despite generating the same mRNA transcripts, suggesting that the splicing of transcripts from a gene positively impacts its expression. Termed intron-mediated enhancement (IME), this phenomenon has been observed in animals, plants, and fungi, with enhancement ranging from 2- to 100-fold and the strongest effects observed for promoter-proximal introns1-3. Here, we assayed thousands of natural human introns, covering all distinct human 5' splice site (5SS) motifs and thousands of 3' splice sites (3SS), for their ability to enhance (or repress) expression of a reporter gene. The results confirmed previous observations that intronic UUUU motifs augment the magnitude of IME, and revealed two fundamental features of IME: (i) only introns spliced by the major spliceosome elicit IME, with minor introns often repressing expression; and (ii) the magnitude of IME increases with the strength of the 5SS motif, but is independent of 3SS strength. Our findings imply that genetic variants that alter the strength of the first 5SS of genes may alter their expression by modulating IME without necessarily impacting splicing. We confirm this expectation using human population genetic data and further show that disease-relevant perturbations of U1 snRNP components that enhance recognition of subsets of first 5SS up-regulate expression of the associated genes. These findings demonstrate that recognition of the first 5SS is vitally important to the expression of human genes.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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