(524.9) Developing a Pipeline for Personalized Diagnoses of Glut1 Deficiency Syndrome

Poster Board Number: A502

Laiken Griffith (University of Kentucky College of Medicine), Katherine Donohue (University of Kentucky College of Medicine), Maya Abul-Khoudoud (University of Kentucky College of Medicine), Matthew Gentry (University of Kentucky College of Medicine)

Glut1 deficiency syndrome (Glut1DS) is a brain metabolic disorder, caused by the impairment of glucose transport in the brain. Glut1DS occurs when there is a mutation in the SLC2A1 gene, which encodes for the protein glucose transporter protein type 1. Around 500 people worldwide suffer from this disease, and most deal with symptoms such as seizures, movement disorders, and in more severe cases, microcephaly. A common treatment for Glut1DS is the ketogenic diet, which provides the brain with an alternate energy source. Patients suffering from Glut1DS have been shown to respond more positively to this treatment when they are able to start it early in life.

Unfortunately, due to the variety of phenotypes associated with Glut1DS, there is often a large gap between onset of symptoms and diagnosis. This is problematic because a delay in diagnosis means a delay in treatment, leading to more severe disease progression. In order to bridge the gap, our lab applied a suite of bioinformatics techniques to characterize novel mutations and help patients achieve faster diagnoses. Because Glut1DS patients with different mutations experience varying loss of function of the Glut1 transporter, linking mutation type to phenotype could help patients receive a faster diagnosis, allowing them to start treatment earlier.

In order to do this, we analyzed and classified different mutations in the SLC2A1 gene based on predicted pathogenicity. By comparing these data to known Glut1DS patient phenotypes, we were able to able to define five mutation classes based on severity. This type of analysis can be used to provide a faster diagnosis and personalized treatment plans for patients based on their mutation class. As personalized medicine for rare genetic diseases progresses, a database of mutations and severity class could help patients start life-changing treatment much earlier. Additionally, this model for pipeline development could be expanded to assist in the diagnosis of other rare genetic diseases.

This study was supported by National Institute of Neurological Disorders and Stroke (R35 NS116824, P01 NS097197-01), National Science Foundation (MCB-1817414), and NIH National Center for Advancing Translational Sciences (UL1TR001998).