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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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.
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