1. Cell Biology
  2. Computational and Systems Biology

An in silico FSHD muscle fibre for modelling DUX4 dynamics and predicting the impact of therapy

  1. Matthew V Cowley
  2. Johanna Pruller
  3. Massimo Ganassi
  4. Peter S Zammit
  5. Christopher RS Banerji  Is a corresponding author
  1. University of Bath, United Kingdom
  2. King's College London, United Kingdom
  3. The Alan Turing Institute, United Kingdom
https://doi.org/10.7554/eLife.88345
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  1. Matthew V Cowley
  2. Johanna Pruller
  3. Massimo Ganassi
  4. Peter S Zammit
  5. Christopher RS Banerji
(2023)
An in silico FSHD muscle fibre for modelling DUX4 dynamics and predicting the impact of therapy
eLife 12:e88345.
https://doi.org/10.7554/eLife.88345
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Abstract

Facioscapulohumeral muscular dystrophy (FSHD) is an incurable myopathy linked to over-expression of the myotoxic transcription factor DUX4. Targeting DUX4 is the leading therapeutic approach, however it is only detectable in 0.1-3.8% of FSHD myonuclei. How rare DUX4 drives FSHD and the optimal anti-DUX4 strategy is unclear. We combine stochastic gene expression with compartment models of cell states, building a simulation of DUX4 expression and consequences in FSHD muscle fibres. Investigating iDUX4 myoblasts, scRNAseq and snRNAseq of FSHD muscle we estimate parameters including DUX4 mRNA degradation, transcription and translation rates and DUX4 target gene activation rates. Our model accurately recreates the distribution of DUX4 and target gene positive cells seen in scRNAseq of FSHD myocytes. Importantly we show DUX4 drives significant cell death despite expression in only 0.8% of live cells. Comparing scRNAseq of unfused FSHD myocytes to snRNAseq of fused FSHD myonuclei, we find evidence of DUX4 protein syncytial diffusion and estimate its rate via genetic algorithms. We package our model into freely available tools, to rapidly investigate consequences of anti-DUX4 therapy.

Data availability

All data generated or analysed during this study are publicly available or included in the manuscript, all code employed is published as part of our shiny app at 3 public domain URLs listed in the manuscript.

The following previously published data sets were used

Article and author information

Author details

  1. Matthew V Cowley

    Department of Chemistry, University of Bath, Bath, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5258-8024

  2. Johanna Pruller

    Randall Centre for Cell and Molecular Biophysics, King's College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  3. Massimo Ganassi

    Randall Centre for Cell and Molecular Biophysics, King's College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3163-9707

  4. Peter S Zammit

    Randall Centre for Cell and Molecular Biophysics, King's College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9562-3072

  5. Christopher RS Banerji

    The Alan Turing Institute, London, United Kingdom
    For correspondence
    cbanerji@turing.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4373-7657

Funding

EPSRC Centre for Doctoral Training in Sustainable Chemical Technologies (EP/L016354/1)

  • Matthew V Cowley

Friends of FSH Research

  • Matthew V Cowley

Muscular Dystrophy UK (19GRO-PG12-0493)

  • Johanna Pruller

FSHD Society (FSHD-Winter2021-4491649104)

  • Johanna Pruller

Medical Research Council (MR/S002472/1)

  • Massimo Ganassi

Association Francaise contre les Myopathies

  • Peter S Zammit

SOLVE FSHD

  • Massimo Ganassi

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

Copyright

© 2023, Cowley 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. Matthew V Cowley
  2. Johanna Pruller
  3. Massimo Ganassi
  4. Peter S Zammit
  5. Christopher RS Banerji
(2023)
An in silico FSHD muscle fibre for modelling DUX4 dynamics and predicting the impact of therapy
eLife 12:e88345.
https://doi.org/10.7554/eLife.88345

Share this article

https://doi.org/10.7554/eLife.88345

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