Automatic text mining approach to identify molecular target candidates associated with metabolic process for Myotonic Dystrophy type 1

Topic:

Other

Poster Number: Virtual

Author(s):

Dhvani H Kuntawala, MSc, BSc, Medical Sciences Department, Institute of Biomedicine – iBiMED, University of Aveiro, Sandra Rebelo, PhD, Medical Sciences Department, Institute of Biomedicine – iBiMED, University of Aveiro, Rui Vitorino, PhD, Medical Sciences Department, Institute of Biomedicine – iBiMED, University of Aveiro, Filipa Martins, PhD, Medical Sciences Department, Institute of Biomedicine – iBiMED, University of Aveiro, 3810-183 Avei

Background and Aim: Myotonic dystrophy type 1 (DM1) is an autosomal dominant hereditary disease caused by abnormal expansion of unstable CTG repeats in the 3’ untranslated region of Myotonic Dystrophy protein kinase (DMPK) gene. This disease mainly affects the skeletal muscle, resulting in myotonia, progressive distal muscle weakness and atrophy but also affects other tissues and systems like heart and central nervous system. Despite some studies reporting therapeutic strategies for the treatment of DM1, many issues remain unsolved such as the contribution of metabolic and mitochondria dysfunctions for DM1 pathogenesis. Thus, it is crucial to identify molecular target candidates associated with metabolic process for DM1 process using an unbiased strategy of automatic text mining previously used with bibliometrics studies. Material/Methods: Resorting to bibliometric analysis, articles combining DM1, and metabolic/metabolism terms were identified and further analyzed using the VOSviewer software. The list of molecular target candidates for DM1 associated to metabolic/metabolism was generated, which was compared with genes previously associated with DM1 in DisGeNET database. Further, the g: Profiler was used to perform a functional enrichment analysis by using the Gene Ontology (GO) and REAC databases. Enriched signaling pathways were identified using the integrated bioinformatics enrichment analyses. Results: The results revealed that only 15 of the genes identified in the bibliometrics analysis were previously associated with DM1 in DisGeNET database. Of note, we identified 71 genes not previously associated with DM1 in DisGeNET, which are of particular interest and will be further evaluated. The functional enrichment analysis of these genes revealed that regulation of cellular metabolic, and metabolic process were the top biological processes associated. Also, a number of signaling pathways were found enriched. Conclusion: Overall, the identification of several valuable target candidates related with metabolic process for DM1. Therefore, our study has strengthened the hypothesis that metabolic dysfunctions might contribute for DM1 pathogenesis, and the exploitation of metabolic dysfunctions targets are crucial for the development of future therapeutic interventions for DM1.