Epilepsy is a common primary neurological disorder characterized by the chronic tendency of a patient to experience epileptic seizures, which are abnormal body movements or cognitive states that result from excessive, hypersynchronous brain activity. Epilepsy has been found to have numerous etiologies and, although about two-thirds of epilepsies were classically considered idiopathic, the majority of those are now believed to be of genetic origin. Mutations in genes involved in gamma-aminobutyric acid (GABA)-mediated inhibitory neurotransmission have been associated with a broad range of epilepsy syndromes. Mutations in the GABA-A receptor gamma 2 subunit gene (GABRG2), for example, have been associated with absence epilepsy and febrile seizures in humans. Several rodent models of GABRG2 loss of function depict clinical features of the disease; however, alternative genetic models more amenable for the study of ictogenesis and for high-throughput screening purposes are still needed. In this context, we generated a gabrg2 knockout (KO) zebrafish model (which we called R23X) that displayed light/dark-induced reflex seizures. Through high-resolution in vivo calcium imaging of the brain, we showed that this phenotype is associated with widespread increases in neuronal activity that can be effectively alleviated by the anti-epileptic drug valproic acid. Moreover, these seizures only occur at the larval stages but disappear after 1 week of age. Interestingly, our whole-transcriptome analysis showed that gabrg2 KO does not alter the expression of genes in the larval brain. As a result, the gabrg2−/− zebrafish is a novel in vivo genetic model of early epilepsies that opens new doors to investigate ictogenesis and for further drug-screening assays.

The genetic diagnosis of patients with seizure disorders has been improved significantly by the development of affordable next-generation sequencing technologies. Indeed, in the last 20 years, dozens of causative genes and thousands of associated variants have been described and, for many patients, are now considered responsible for their disease. However, the functional consequences of these mutations are often not studied in vivo, despite such studies being central to understanding pathogenic mechanisms and identifying novel therapeutic avenues. One main roadblock to functionally characterizing pathogenic mutations is generating and characterizing in vivo mammalian models carrying clinically relevant variants in specific genes identified in patients. Although the emergence of new mutagenesis techniques facilitates the production of rodent mutants, the fact that early development occurs internally hampers the investigation of gene function during neurodevelopment. In this context, functional genomics studies using simple animal models such as flies or fish are advantageous since they open a dynamic window of investigation throughout embryonic development. In this review, we will summarize how the use of simple animal models can fill the gap between genetic diagnosis and functional and phenotypic correlates of gene function in vivo. In particular, we will discuss how these simple animals offer the possibility to study gene function at multiple scales, from molecular function (i.e., ion channel activity), to cellular circuit and brain network dynamics. As a result, simple model systems offer alternative avenues of investigation to model aspects of the disease phenotype not currently possible in rodents, which can help to unravel the pathogenic substratum in vivo.

Excitation-inhibition (EI) balance may be required for the organisation of brain dynamics to a phase transition, criticality, which confers computational benefits. Brain pathology associated with EI imbalance may therefore occur due to a deviation from criticality. However, evidence linking critical dynamics with EI imbalance-induced pathology is lacking. Here, we studied the effect of EI imbalance-induced epileptic seizures on brain dynamics, using in vivo whole-brain 2-photon imaging of GCaMP6s larval zebrafish at single-neuron resolution. We demonstrate the importance of EI balance for criticality, with EI imbalance causing a loss of whole-brain critical statistics. Using network models we show that a reorganisation of network topology drives this loss of criticality. Seizure dynamics match theoretical predictions for networks driven away from a phase transition into disorder, with the emergence of chaos and a loss of network-mediated separation, dynamic range and metastability. These results demonstrate that EI imbalance drives a pathological deviation from criticality.

No abstract