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Original research
Identifying the molecular drivers of ALS-implicated missense mutations
  1. Stephanie Portelli1,2,3,
  2. Amanda Albanaz4,
  3. Douglas Eduardo Valente Pires1,5,
  4. David Benjamin Ascher1,2,3
  1. 1Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
  2. 2SCMB, The University of Queensland, Saint Lucia Campus, Saint Lucia, Queensland, Australia
  3. 3Systems and Computational Biology, Bio21 Institute, The University of Melbourne, Parkville, Victoria, Australia
  4. 4Instituto René Rachou, FIOCRUZ, Belo Horizonte, Brazil
  5. 5School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
  1. Correspondence to Prof David Benjamin Ascher, School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, QLD, Australia; d.ascher{at}uq.edu.au; Dr Stephanie Portelli; s.portelli{at}uq.edu.au; Dr Douglas Eduardo Valente Pires; douglas.pires{at}unimelb.edu.au

Abstract

Background Amyotrophic lateral sclerosis (ALS) is a progressively fatal, neurodegenerative disease associated with both motor and non-motor symptoms, including frontotemporal dementia. Approximately 10% of cases are genetically inherited (familial ALS), while the majority are sporadic. Mutations across a wide range of genes have been associated; however, the underlying molecular effects of these mutations and their relation to phenotypes remain poorly explored.

Methods We initially curated an extensive list (n=1343) of missense mutations identified in the clinical literature, which spanned across 111 unique genes. Of these, mutations in genes SOD1, FUS and TDP43 were analysed using in silico biophysical tools, which characterised changes in protein stability, interactions, localisation and function. The effects of pathogenic and non-pathogenic mutations within these genes were statistically compared to highlight underlying molecular drivers.

Results Compared with previous ALS-dedicated databases, we have curated the most extensive missense mutation database to date and observed a twofold increase in unique implicated genes, and almost a threefold increase in the number of mutations. Our gene-specific analysis identified distinct molecular drivers across the different proteins, where SOD1 mutations primarily reduced protein stability and dimer formation, and those in FUS and TDP-43 were present within disordered regions, suggesting different mechanisms of aggregate formation.

Conclusion Using our three genes as case studies, we identified distinct insights which can drive further research to better understand ALS. The information curated in our database can serve as a resource for similar gene-specific analyses, further improving the current understanding of disease, crucial for the development of treatment strategies.

  • Genetic Predisposition to Disease
  • Genetics, Medical

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information. All data collected and generated in this study are available within online supplemental table 1.xlsx.

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Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information. All data collected and generated in this study are available within online supplemental table 1.xlsx.

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Footnotes

  • Twitter @DrDavidAscher

  • Contributors SP was responsible for data curation, development and validation of predictive models, and preparation of the draft manuscript. AA assisted with data curation. DEVP assisted with machine learning. DBA designed and supervised all aspects of the project. Guarantor: DBA.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.