Allelik Heterojenitenin Gözlendiği Kas Distrofilerinin Biyoenformatik Araçlar Kullanılarak Araştırılması
Çalı Daylan, Ayşe Ece
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Limb-girdle muscle dystrophy (LGMD) is a clinically and genetically heterogeneous group of inherited muscle disorders characterized by progressive muscle degeneration predominantly in proximal muscles of shoulder and pelvic girdle. Despite numerous novel genes recently associated with autosomal recessively inherited LGMD (LGMD2), there are still LGMD patients without a defined genetic cause. This demonstrates the necessity to identify novel genes that can cause muscle dystrophy phenotype. Besides novel gene identification studies, it is necessary to study the roles of these gene products in muscle cell integrity. Since there is no treatment available that can alter the disease progression and that the cellular functions of novel genes are not known, LGMD2 is a disease group that needs further study. Revealing the gene functions and molecular pathogenesis of the disease will help design new treatment methods. This study aims to determine the cellular pathways and their key driver genes that play a role in the pathogenesis of dysferlinopathy, which is one of the allelic muscle dystrophies. Towards this purpose, text mining, which is a recently progressing field that can reveal information hidden in the literature and which is predicted to become the routine tool of biomedical research in near future, has been employed. In addition, in order to determine the cellular pathways and key genes related to dysferlinopathy, weighted gene co-expression network analysis has been used. A poorly characterized gene that has recently been associated with muscle dystrophy, TOR1AIP1, has been identified as one of the hub genes in dysferlinopathy pathogenesis. This enabled us to use co-expression network analysis in order to predict muscle specific functions of TOR1AIP1, based on guilt-by-association principle that states genes having similar expression profiles have similar functions. In silico gene function prediction is enriched by protein analysis tools that predicts biological processes in which the protein is active based on protein sequence features. This study has determined the main cellular pathways in dysferlinopathy pathogenesis using bioinformatics approach. By elucidating the key driver genes, possible therapeutic targets and biomarkers have been identified for dysferlinopathy. In addition, it is suggested that TOR1AIP1 may have a role in SMAD4 dependent signaling.