Genome-Wide Association between Branch Point Properties and Alternative Splicing

Abstract

The branch point (BP) is one of the three obligatory signals required for pre-mRNA splicing. In mammals, the degeneracy of the motif combined with the lack of a large set of experimentally verified BPs complicates the task of modeling it in silico, and therefore of predicting the location of natural BPs. Consequently, BPs have been disregarded in a considerable fraction of the genome-wide studies on the regulation of splicing in mammals. We present a new computational approach for mammalian BP prediction. Using sequence conservation and positional bias we obtained a set of motifs with good agreement with U2 snRNA binding stability. Using a Support Vector Machine algorithm, we created a model complemented with polypyrimidine tract features, which considerably improves the prediction accuracy over previously published methods. Applying our algorithm to human introns, we show that BP position is highly dependent on the presence of AG dinucleotides in the 3′ end of introns, with distance to the 3′ splice site and BP strength strongly correlating with alternative splicing. Furthermore, experimental BP mapping for five exons preceded by long AG-dinucleotide exclusion zones revealed that, for a given intron, more than one BP can be chosen throughout the course of splicing. Finally, the comparison between exons of different evolutionary ages and pseudo exons suggests a key role of the BP in the pathway of exon creation in human. Our computational and experimental analyses suggest that BP recognition is more flexible than previously assumed, and it appears highly dependent on the presence of downstream polypyrimidine tracts. The reported association between BP features and the splicing outcome suggests that this, so far disregarded but yet crucial, element buries information that can complement current acceptor site models.

Author Summary

From transcription to translation, the events underlying protein production from DNA sequence are paramount to all aspects of cellular function. Pre-mRNAs in eukaryotes undergo several processing steps prior to their export to the cytoplasm. Among these, splicing – the process of intron removal and exon ligation – has been shown to play a central role in the regulation of gene expression. It has been estimated that more than half of the disease-causing mutations in humans do so by interfering with splicing. The difficulty in describing these disease mechanisms often lies in the low accuracy of the methods for prediction of functional splicing signals in the pre-mRNA. This is especially the case of the branch point, mainly due to its high sequence variability. We have developed a methodology for mammalian branch point prediction based on a machine-learning algorithm, which shows improved accuracy over previous published methods. Moreover, using a combination of experimental and bioinformatics approaches, we uncovered important positional properties of the branch point and shed new light on how some of its features may contribute to the final splicing outcome. These findings might prove useful for a better understanding of how splicing-associated mutations can lead to disease.