Automatic Text-Mining Approach to Identify Molecular Target Candidates Associated with Metabolic Processes for Myotonic Dystrophy Type 1

Abstract

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 the myotonic dystrophy protein kinase (DMPK) gene. This disease mainly affects skeletal muscle, resulting in myotonia, progressive distal muscle weakness, and atrophy, but also affects other tissues and systems, such as the heart and central nervous system. Despite some studies reporting therapeutic strategies for DM1, many issues remain unsolved, such as the contribution of metabolic and mitochondrial dysfunctions to DM1 pathogenesis. Therefore, it is crucial to identify molecular target candidates associated with metabolic processes for DM1. In this study, resorting to a bibliometric analysis, articles combining DM1, and metabolic/metabolism terms were identified and further analyzed using an unbiased strategy of automatic text mining with VOSviewer software. A list of candidate molecular targets for DM1 associated with metabolic/metabolism was generated and compared with genes previously associated with DM1 in the DisGeNET database. Furthermore, g:Profiler was used to perform a functional enrichment analysis using the Gene Ontology (GO) and REAC databases. Enriched signaling pathways were identified using integrated bioinformatics enrichment analyses. The results revealed that only 15 of the genes identified in the bibliometric analysis were previously associated with DM1 in the DisGeNET database. Of note, we identified 71 genes not previously associated with DM1, which are of particular interest and should be further explored. The functional enrichment analysis of these genes revealed that regulation of cellular metabolic and metabolic processes were the most associated biological processes. Additionally, a number of signaling pathways were found to be enriched, e.g., signaling by receptor tyrosine kinases, signaling by NRTK1 (TRKA), TRKA activation by NGF, PI3K-AKT activation, prolonged ERK activation events, and axon guidance. Overall, several valuable target candidates related to metabolic processes for DM1 were identified, such as NGF, NTRK1, RhoA, ROCK1, ROCK2, DAG, ACTA, ID1, ID2 MYOD, and MYOG. Therefore, our study strengthens the hypothesis that metabolic dysfunctions contribute to DM1 pathogenesis, and the exploitation of metabolic dysfunction targets is crucial for the development of future therapeutic interventions for DM1.