bioRxiv preprint doi: https://doi.org/10.1101/2021.01.14.425874; this version posted January 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias Nikoleta Vavouraki1,2, James E. Tomkins1, Eleanna Kara3, Henry Houlden3, John Hardy4, Marcus J. Tindall2,5, Patrick A. Lewis1,4,6, Claudia Manzoni1,7* Author Affiliations 1: Department of Pharmacy, University of Reading, Reading, RG6 6AH, United Kingdom 2: Department of Mathematics and Statistics, University of Reading, Reading, RG6 6AH, United Kingdom 3: Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, WC1N 3BG, United Kingdom 4: Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, WC1N 3BG, United Kingdom 5: Institute of Cardiovascular and Metabolic Research, University of Reading, Reading, RG6 6AS, United Kingdom 6: Department of Comparative Biomedical Sciences, Royal Veterinary College, London, NW1 0TU, United Kingdom 7: School of Pharmacy, University College London, London, WC1N 1AX, United Kingdom *Corresponding author:
[email protected] Abstract The Hereditary Spastic Paraplegias are a group of neurodegenerative diseases characterized by spasticity and weakness in the lower body. Despite the identification of causative mutations in over 70 genes, the molecular aetiology remains unclear. Due to the combination of genetic diversity and variable clinical presentation, the Hereditary Spastic Paraplegias are a strong candidate for protein- protein interaction network analysis as a tool to understand disease mechanism(s) and to aid functional stratification of phenotypes.