The kinetics of pre-mRNA splicing in the Drosophila genome and the influence of gene architecture
- Massachusetts Institute of Technology, United States
- National Institute for Environmental Health Sciences, United States
- Harvard Medical School, United States
- Cited 9
- Views 3,144
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Abstract
Production of most eukaryotic mRNAs requires splicing of introns from pre-mRNA. The splicing reaction requires definition of splice sites, which are initially recognized in either intron-spanning ('intron definition') or exon-spanning ('exon definition') pairs. To understand how exon and intron length and splice site recognition mode impact splicing, we measured splicing rates genome-wide in Drosophila, using metabolic labeling/RNA sequencing and new mathematical models to estimate rates. We found that the modal intron length range of 60-70 nt represents a local maximum of splicing rates, but that much longer exon-defined introns are spliced even faster and more accurately. Surprisingly, we observed low variation in splicing rates across introns in the same gene, suggesting the presence of gene-level influences, and we identified multiple gene level variables associated with splicing rate. Together our data suggest that developmental and stress response genes may have preferentially evolved exon definition in order to enhance rates of splicing.
Article and author information
Author details
Christopher B Burge
Funding
National Institutes of Health (Z01-ES101987)
- Telmo Henriques
- Adam Burkholder
- Karen Adelman
National Institutes of Health (R01-GM085319)
- Athma A Pai
- Christopher B Burge
Jane Coffin Childs Memorial Fund for Medical Research
- Athma A Pai
U.S. Department of Energy (FG02-97ER25308)
- Kayla McCue
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Timothy W Nilsen, Case Western Reserve University, United States
Publication history
- Received: October 5, 2017
- Accepted: December 22, 2017
- Accepted Manuscript published: December 27, 2017 (version 1)
- Version of Record published: January 10, 2018 (version 2)
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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Further reading
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- Computational and Systems Biology
- Physics of Living Systems
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- Computational and Systems Biology
- Neuroscience
