Pathogenic variants that alter protein code often disrupt splicing

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Abstract

The lack of tools to identify causative variants from sequencing data greatly limits the promise of Precision Medicine. Previous studies suggest one-third of disease alleles alter splicing. We discovered that splicing defects cluster in diseases (e.g. haploinsufficient genes). We analyzed 4,964 published disease-causing exonic mutations using a Massively Parallel Splicing Assay (MaPSy) that showed 81% concordance rate with patient tissue splicing. ~10% of exonic mutations altered splicing, mostly by disrupting multiple stages of the spliceosome assembly. We present the first large-scale characterization of exonic splicing mutations using a novel technology that facilitates variant classification that keeps pace with variant discovery.

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Most cited references 23

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Predictive identification of exonic splicing enhancers in human genes.

William G. Fairbrother, Ru-Fang Yeh, Phillip A Sharp … (2002)

Specific short oligonucleotide sequences that enhance pre-mRNA splicing when present in exons, termed exonic splicing enhancers (ESEs), play important roles in constitutive and alternative splicing. A computational method, RESCUE-ESE, was developed that predicts which sequences have ESE activity by statistical analysis of exon-intron and splice site composition. When large data sets of human gene sequences were used, this method identified 10 predicted ESE motifs. Representatives of all 10 motifs were found to display enhancer activity in vivo, whereas point mutants of these sequences exhibited sharply reduced activity. The motifs identified enable prediction of the splicing phenotypes of exonic mutations in human genes.

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Systematic identification and analysis of exonic splicing silencers.

Zefeng Wang, Michael E Rolish, Gene Yeo … (2004)

Exonic splicing silencers (ESSs) are cis-regulatory elements that inhibit the use of adjacent splice sites, often contributing to alternative splicing (AS). To systematically identify ESSs, an in vivo splicing reporter system was developed to screen a library of random decanucleotides. The screen yielded 141 ESS decamers, 133 of which were unique. The silencer activity of over a dozen of these sequences was also confirmed in a heterologous exon/intron context and in a second cell type. Of the unique ESS decamers, most could be clustered into groups to yield seven putative ESS motifs, some resembling known motifs bound by hnRNPs H and A1. Potential roles of ESSs in constitutive splicing were explored using an algorithm, ExonScan, which simulates splicing based on known or putative splicing-related motifs. ExonScan and related bioinformatic analyses suggest that these ESS motifs play important roles in suppression of pseudoexons, in splice site definition, and in AS.

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An increased specificity score matrix for the prediction of SF2/ASF-specific exonic splicing enhancers.

Philip J Smith, Chaolin Zhang, Jinhua Wang … (2006)

Numerous disease-associated point mutations exert their effects by disrupting the activity of exonic splicing enhancers (ESEs). We previously derived position weight matrices to predict putative ESEs specific for four human SR proteins. The score matrices are part of ESEfinder, an online resource to identify ESEs in query sequences. We have now carried out a refined functional SELEX screen for motifs that can act as ESEs in response to the human SR protein SF2/ASF. The test BRCA1 exon under selection was internal, rather than the 3'-terminal IGHM exon used in our earlier studies. A naturally occurring heptameric ESE in BRCA1 exon 18 was replaced with two libraries of random sequences, one seven nucleotides in length, the other 14. Following three rounds of selection for in vitro splicing via internal exon inclusion, new consensus motifs and score matrices were derived. Many winner sequences were demonstrated to be functional ESEs in S100-extract-complementation assays with recombinant SF2/ASF. Motif-score threshold values were derived from both experimental and statistical analyses. Motif scores were shown to correlate with levels of exon inclusion, both in vitro and in vivo. Our results confirm and extend our earlier data, as many of the same motifs are recognized as ESEs by both the original and our new score matrix, despite the different context used for selection. Finally, we have derived an increased specificity score matrix that incorporates information from both of our SF2/ASF-specific matrices and that accurately predicts the exon-skipping phenotypes of deleterious point mutations.

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Author and article information

Journal

Journal ID (nlm-journal-id): 9216904

Journal ID (pubmed-jr-id): 2419

Journal ID (nlm-ta): Nat Genet

Journal ID (iso-abbrev): Nat. Genet.

Title: Nature genetics

ISSN (Print): 1061-4036

ISSN (Electronic): 1546-1718

Publication date Nihms-submitted: 22 March 2019

Publication date (Electronic): 17 April 2017

Publication date (Print): June 2017

Publication date PMC-release: 03 August 2019

Volume: 49

Issue: 6

Pages: 848-855

Affiliations

[1 ]Center for Computational Molecular Biology, Brown University, Providence, RI

[2 ]Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI.

[3 ]Department of Computer Engineering, Brown University, Providence, RI.

[4 ]Department of Computer Science, Brown University, Providence, RI.

[5 ]Department of Pathology, University of Utah, School of Medicine, Salt Lake City, UT.

[6 ]Hassenfeld Child Health Innovation Institute of Brown University, Providence, RI.

[7 ]Contributed equally.

Author notes

Author contributions

W.F. and R.S. designed the experiments. R.S. performed MaPSy experiments. R.S., J.W., P.B. and J.M. performed validation experiments. K.C. performed alignment, counting and RBP motif analyses. R.S. performed ESM analyses, machine learning and MaPSy SELEX analyses. C.R. performed HGMD genes analyses. C.B. and J.Y. developed the visualization web browser. W.F. and R.S. wrote the paper with contributions from all authors.

[* ]Correspondence to: william_fairbrother@ 123456brown.edu .

Article

Manuscript ID: NIHMS861030

DOI: 10.1038/ng.3837

PMC ID: 6679692

PubMed ID: 28416821

SO-VID: e3dc6720-b971-4472-84d7-eb7a82f075f2

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Pathogenic variants that alter protein code often disrupt splicing

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Abstract

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Genome Engineering using CRISPR

Most cited references 23

Predictive identification of exonic splicing enhancers in human genes.

Systematic identification and analysis of exonic splicing silencers.

An increased specificity score matrix for the prediction of SF2/ASF-specific exonic splicing enhancers.

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