Quantitative prediction of variant effects on alternative splicing in MAPT using endogenous pre-messenger RNA structure probing

  1. Jayashree Kumar
  2. Lela Lackey
  3. Justin M Waldern
  4. Abhishek Dey
  5. Anthony M Mustoe
  6. Kevin Weeks
  7. David H Mathews
  8. Alain Laederach  Is a corresponding author
  1. University of North Carolina at Chapel Hill, United States
  2. Clemson University, United States
  3. Baylor College of Medicine, United States
  4. University of Rochester, United States

Abstract

Splicing is highly regulated and is modulated by numerous factors. Quantitative predictions for how a mutation will affect precursor messenger RNA (mRNA) structure and downstream function is particularly challenging. Here we use a novel chemical probing strategy to visualize endogenous precursor and mature MAPT mRNA structures in cells. We used these data to estimate Boltzmann suboptimal structural ensembles, which were then analyzed to predict consequences of mutations on precursor mRNA structure. Further analysis of recent cryo-EM structures of the spliceosome at different stages of the splicing cycle revealed that the footprint of the Bact complex with precursor mRNA best predicted alternative splicing outcomes for exon 10 inclusion of the alternatively spliced MAPT gene, achieving 74% accuracy. We further developed a b-regression weighting framework that incorporates splice site strength, RNA structure, and exonic/intronic splicing regulatory elements capable of predicting, with 90% accuracy, the effects of 47 known and six newly discovered mutations on inclusion of exon 10 of MAPT. This combined experimental and computational framework represents a path forward for accurate prediction of splicing-related disease-causing variants.

Data availability

Sequencing data have been deposited in SRA under BioProject ID PRJNA762079 and PRJNA812003.DMS Reactivities are available as SNRNASMs at https://bit.ly/2WaDw6FAll data generated or analyzed during this study are included in the manuscript and supporting files; Source Data files have been provided for Figures 1,2,4,5 and 6.Modeling and feature generation code is uploaded at https://git.io/JuSW8

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Jayashree Kumar

    Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6914-748X
  2. Lela Lackey

    Department of Genetics and Biochemistry, Clemson University, Greenwood, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Justin M Waldern

    Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Abhishek Dey

    Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Anthony M Mustoe

    Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Kevin Weeks

    Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. David H Mathews

    Department of Biochemistry and Biophysics, University of Rochester, Rochester, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Alain Laederach

    Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
    For correspondence
    alain@unc.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5088-9907

Funding

National Institutes of Health (R01 HL111527)

  • Alain Laederach

National Institutes of Health (R35 GM 140844)

  • Alain Laederach

National Institutes of Health (R01 GM076485)

  • David H Mathews

National Institutes of Health (R35 GM122532)

  • Kevin Weeks

Cancer Prevention and Research Institute of Texas (CPRIT Scholar)

  • Anthony M Mustoe

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Jonathan P Staley, University of Chicago, United States

Version history

  1. Preprint posted: September 13, 2021 (view preprint)
  2. Received: September 14, 2021
  3. Accepted: June 12, 2022
  4. Accepted Manuscript published: June 13, 2022 (version 1)
  5. Version of Record published: June 27, 2022 (version 2)

Copyright

© 2022, Kumar et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Jayashree Kumar
  2. Lela Lackey
  3. Justin M Waldern
  4. Abhishek Dey
  5. Anthony M Mustoe
  6. Kevin Weeks
  7. David H Mathews
  8. Alain Laederach
(2022)
Quantitative prediction of variant effects on alternative splicing in MAPT using endogenous pre-messenger RNA structure probing
eLife 11:e73888.
https://doi.org/10.7554/eLife.73888

Share this article

https://doi.org/10.7554/eLife.73888

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