The use of model genetic systems to study gene interactions has proven invaluable in the history of genetics. Generation of genetic mutations and then suppressor or enhancer mutations produce answers, at the whole organism level, to questions of immediate biological relevance. In addition, the nature of genetic modifier genes and mutations may reveal novel mechanistic insights. Our laboratory has pioneered the use of homologous recombination (HR) to introduce human epilepsy-causing mutations into the Drosophila genome, generating robust seizure phenotypes. Such a system of accurate genetic modeling in a complex animal creates unique opportunities for systematic analyses of mechanisms of epileptogenesis and avenues to novel therapies. Here, we propose to utilize Drosophila human epilepsy models in genome wide systematic screens for transcriptomic changes in response to seizure, as well as forward genetic screens for mutations in genes that are capable of suppressing ("genetically curing") the phenotype conferred by human epilepsy mutations. These discovery-based approaches, while high risk, are based solidly on the conservation of genes and processes, as the mutant phenotypes demonstrate. Our EUREKA proposal contains the potential to reveal new insights into the development of epilepsy, compensatory mechanisms to offset perturbed excitability, and alterations in genes and processes capable of reversing the effects of human epilepsy mutations.
Our laboratory has innovated a method to introduce human epilepsy-causing mutations directly into the fruit fly genome. Such mutant animals display symptoms remarkably like human epilepsy. We propose utilize this system along with whole-genome and genetic approaches to identify genes involved with seizure, and those capable of reversing the effects of epilepsy.
|Schutte, Ryan J; Schutte, Soleil S; Algara, Jacqueline et al. (2014) Knock-in model of Dravet syndrome reveals a constitutive and conditional reduction in sodium current. J Neurophysiol 112:903-12|
|Sugden, Lauren A; Tackett, Michael R; Savva, Yiannis A et al. (2013) Assessing the validity and reproducibility of genome-scale predictions. Bioinformatics 29:2844-51|