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eSymposia | Proteomics in Cell Biology and Disease


Hereditary Spastic Paraplegias: insights through a bioinformatic approach


Sep 21, 2020 12:00am ‐ Sep 21, 2020 12:00am

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Hereditary Spastic Paraplegias: insights through a bioinformatic approach Nikoleta Vavouraki1, James Tomkins1, Eleanna Kara2, Henry Houlden2, John Hardy2, Marcus Tindall3, Patrick Lewis4,2,1 and Claudia Manzoni5,1 1: School of Pharmacy, University of Reading, Whiteknights, RG6 6AH, UK 2: UCL Institute of Neurology, University College London, Queen Square, WC1N 3BG, UK 3: Department of Mathematics and Statistics, University of Reading, Whiteknights, RG6 6AH, UK 4: Royal Veterinary College, NW1 0TU, UK 5: UCL School of Pharmacy, University College London, WC1N 1AX Background: Protein-protein interactions (PPIs) are fundamental to allow protein functionality in the context of complexes and pathways. Therefore, the study of PPI networks (PPIN) formulated around sets of proteins coded by genes implicated in disease could unveil the functional pathways involved in disease mechanism(s). PPIN analysis is ideal for the mechanistical understanding of complex diseases, such as the Hereditary Spastic Paraplegias (HSPs), a group of neurodegenerative diseases that lead to spasticity in the lower limbs. Although more than 70 genetic types of HSPs have been reported, the underlying molecular mechanisms remain elusive and no drugs are available. Methodology / Results: The HSP-PPIN was generated with PPI data for all the associated disease-genes, as produced by PINOT, a novel online bioinformatic tool that provides a list of unique and scored PPIs for the all genes of interest. The analysis of the HSP-PPIN was based on functional enrichment and machine learning tools. The former unveiled functions and pathways highly enriched in the network, supporting some of the suggested disease mechanisms (i.e. intracellular active transport and endolysosomal trafficking pathway), while failing to support others. Studying the relevant/enriched functions of the part of the HSP-network with specific clinical features using a combination of PPIN analysis and machine learning techniques (i.e. PCA and hierarchical clustering) suggested that particular functions/pathways segregate with distinct sets of clinical presentations. Conclusion: PPIN analysis could be beneficial for the mechanistic understanding of complicated and orphan diseases, including the discovery of mechanistic variants depending on the clinical presentations in individual patients. Such an approach could guide drug development, but also aid prioritisation of novel candidate genes to tackle missing heritability of complex diseases. Funding sources: EPSRC, UCL, MRC, Dolby Foundation

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