Κατεύθυνση ΒιοπληροφορικήLibrary of the School of Science
Κωνσταντίνος Βοργιάς, καθηγητής, τομέας Βιοχημείας και Μοριακής Βιολογίας, τμήμα Βιολογίας, Εθνικό και Καποδιστριακό Πανεπιστήμιο
Υπολογιστικές μέθοδοι για βελτιστοποίηση γενετικών θεραπειών για τη μυϊκή δυστροφία Duchenne
Computational methods for optimization of therapeutic approaches for Duchenne Muscular Dystrophy
Duchenne Muscular Dystrophy, a lethal genetic disorder that affects 1 in 3.500 male
newborns worldwide, is caused by mutations in the biggest human gene, called dystrophin. Symptom onset begins as early as 3 years old and the life expectancy is 25 years, while there is no known cure. Exon skipping is a promising approach among the many genetic approaches that are being
tested. Exon skipping means the “skipping” of one or more exons by the splicing machinery, so that the reading frame is corrected and a truncated, but fuctional protein is produced. It is accomplised by Antisense oligonucleotides, that enter the cell and bind to the target exon due to complementary bases. Conjugating these oligonucleotides with Cell Penetrating Peptides, a diverse class of peptides, we can increase their cellular uptake. Our aim is to examine, and predict based on this examination, which of the features of Cell
Penetrating Peptides allow them to be used successfully for the treatment of Muscular Dystrophy. With experimental data obtained by treating mdx mice, a model organism for Duchenne Muscalar Dystrophy, and then using these data to train different classification algorithms, we are trying to build a prediction model. The way that we encode the peptide sequence appears to highly affect the model accuracy,
as well as the use of the experimental conditions like the dose and dose frequency. Our results confirm the importance of amino acid Arginine but also shows the importance of the age in which an mdx mouse is treated. A better understanding of the way that these peptides enter the cell, a larger dataset,
information about other muscles affected by dystrophin absence like cardiac muscle and data about toxicity of Cell Penetrating Peptides would help improve the models we build.
Main subject category:
muscular dystrophy, exon skipping, machine learning, CPP
File access is restricted only to the intranet of UoA.