Abstract

The spliceosome catalyzes the removal of introns from pre-messenger RNA (mRNA) and subsequent pairing of exons with remarkable fidelity. Some exons are known to be skipped or included in the mature mRNA in a cell type- or context-dependent manner (cassette exons), thereby contributing to the diversification of the human proteome. Interestingly, splicing is initiated (and sometimes completed) co-transcriptionally. Here, we develop a kinetic mathematical modeling framework to investigate alternative co-transcriptional splicing (CTS) and, specifically, the control of cassette exons’ inclusion. We show that when splicing is co-transcriptional, default splice patterns of exon inclusion are more likely than when splicing is post-transcriptional, and that certain exons are more likely to be regulatable (i.e. cassette exons) than others, based on the exon–intron structure context. For such regulatable exons, transcriptional elongation rates may affect splicing outcomes. Within the CTS paradigm, we examine previously described hypotheses of co-operativity between splice sites of short introns (i.e. ‘intron definition’) or across short exons (i.e. ‘exon definition’), and find that models encoding these faithfully recapitulate observations in the fly and human genomes, respectively.

Highlights

  • Eukaryotic genes are organized into coding exons, separated by non-coding intron sequences that are removed from the pre-messenger RNA via splicing to produce the mature mRNA

  • To develop a model of co-transcriptional of alternative splicing (CTAS), we first mapped out the reaction diagram (Figure 1)

  • The first, more detailed co-transcriptional alternative splicing (CTAS) model includes reactions occurring at splice sites that are required as a prerequisite to two splice sites being properly paired

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Summary

Introduction

Eukaryotic genes are organized into coding exons, separated by non-coding intron sequences that are removed from the pre-messenger RNA (mRNA) via splicing to produce the mature mRNA. The most successful models have employed a statistical learning approach that side-steps an understanding of underlying molecular mechanisms [5,6]. These models involve RBP–RNA interaction parameters and a variety of genetic and epigenetic features as input parameters for the statistical learning algorithm. The importance of such parameters in the model suggest that gene structure, chromatin and RNA polymerase play a role in determining splicing outcomes. The mechanistic underpinnings of these statistical parameters remain unclear

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