Epileptogenesis and drug discovery in genetic epilepsy models in zebrafish

遗传性癫痫的临床遗传学研究发现，新候选基因的突变数量正在稳步增加，这就需要一种有效的方法来快速提供有关其体内功能的功能数据。此外，鉴于导致耐药癫痫的大量遗传变异，迫切需要一种有效的遗传性癫痫药物发现方法。在这里，我们将(I)生成新的遗传性癫痫(ZF)模型，重点关注(Ia)临床遗传学确定的遗传性癫痫的新候选基因，以及(Ib)通过生物信息学建模方法从人类遗传和转录数据中确定的候选或修饰基因和治疗切入点(任务1)；(Ii)使用高通量行为分析和脑电(任务2)表征这些模型；(3)利用高分辨率全脑钙成像、转基因报告ZF品系的深入形态特征和ZF幼虫脑组织的单细胞RNA-SEQ，对选定的3个模型进行“深表型”，以揭示癫痫的发生机制(任务3)；以及(Iv)通过已知的抗癫痫药物(AEDs)候选药物和新药物线索的药理学描述来表征这些模型，并使用一个选定的ZF模型作为体内生物测定，用于从抗癫痫药物植物中中等通量地筛选药物再用途文库和类药物天然产物文库，以确定对当前耐药患者具有潜在疗效的新型抗癫痫药物线索(任务4)。对于新的候选基因(任务1a)，基于来自P1、P2和P6的数据，我们将创建新的ZF模型来研究SCN8A(编码电压门控Na+通道NaV1.6)、Ash11(编码组蛋白赖氨酸N-甲基转移酶)、KCNQ5(编码电压门控K+通道KV7.5)和KCNA2(编码电压门控K+通道KV1.2)的突变。对于候选修饰基因和治疗切入点(任务1b)，基于P2和P3的数据，我们将生成LOF ZF模型，以在体内阐明最多五个基因作为遗传性癫痫候选治疗切入点或修饰基因的作用，部分是基于患者和小鼠模型中确定的差异表达基因(DEG)的生物信息学优先顺序。将对SCN1A的现有LOF ZF模型以及SCN8A的新LOF和GOF ZF模型进行深度表型分析(任务3)，结果将与SCN1A、SCN2A和SCN8A小鼠模型(P5-8和Z2)进行比较。大规模药物筛选(任务4)将与SCN8A LOF ZF模型一起完成，以满足这些耐药患者有效治疗的临床需求，活性化合物将在SCN8A和SCN2A小鼠模型(P5-8)中进行测试。

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).