Epileptogenesis and drug discovery in genetic epilepsy models in zebrafish

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Clinical genetic studies of genetic epilepsies have identified a steadily growing number of mutations in new candidate genes, creating a need for an effective approach to rapidly provide functional data as to their in vivo function. Furthermore, there is an urgent need for an effective drug discovery approach for genetic epilepsies, given the large number of genetic variations that give rise to drug-resistant epilepsies. Here, we will (i) generate new zebrafish (ZF) models of genetic epilepsies, with a focus on (ia) novel candidate genes for genetic epilepsies identified by clinical genetics, and (ib) candidate or modifier genes and therapeutic entry points identified by bioinformatic modeling approaches from human genetic and transcriptomic data (Task 1); (ii) characterize these models using high-throughput behavioral analysis and EEG (Task 2); (iii) carry out "deep phenotyping" of 3 selected models using high-resolution whole-brain Ca2+ imaging, in-depth morphological characterization in transgenic reporter ZF lines, and single-cell RNA-seq from microdissected larval ZF brains to reveal epileptogenic mechanisms (Task 3); and (iv) characterize these models via pharmacological profiling with known anti-epileptic drugs (AEDs) drug candidates and novel drug leads, and use one selected ZF model as an in vivo bioassay for medium-throughput screening of both a drug repurposing library and a drug-like natural product library from anti-epileptic medicinal plants for the identification of novel anti-epileptic drug leads with potential efficacy in currently drug-resistant patients (Task 4). For the novel candidate genes (Task 1a), based on data from P1, P2 and P6, we will create new ZF models to investigate mutations in SCN8A (encoding the voltage-gated Na+ channel NaV1.6), ASH1L (encoding a histone-lysine N-methyltransferase); KCNQ5 (encoding the voltage-gated K+ channel KV7.5); and KCNA2 (encoding the voltage-gated K+ channel KV1.2). For the candidate modifier genes and therapeutic entry points (Task 1b), based on data from P2 and P3, we will generate LOF ZF models to elucidate in vivo the role of up to five genes identified by bioinformatic modeling approaches as candidate therapeutic entry points or modifiers for genetic epilepsies, based in part on the bioinformatic prioritization of differentially expressed genes (DEGs) identified in patients and mouse models. Deep phenotyping (Task 3) will be carried out on an existing LOF ZF model for SCN1A, and on new LOF and GOF ZF models for SCN8A, with findings to be compared with those generated from SCN1A, SCN2A and SCN8A mouse models (P5-8 and Z2). The large-scale drug screen (Task 4) will be done with the SCN8A LOF ZF model, to address the clinical need for effective therapies for these drug-resistant patients, with active compounds to be tested in SCN8A and SCN2A mouse models (P5-8).