Pericardial Injection of Kainic Acid Induces a Chronic Epileptic State in Larval Zebrafish
Bibliographic record
Abstract
Epilepsy is a common disorder of the brain characterized by spontaneous recurrent seizures, which develop gradually during a process called epileptogenesis. The mechanistic processes underlying the changes of brain tissue and networks toward increased seizure susceptibility are not fully understood. In rodents, injection of kainic acid (KA) ultimately leads to the development of spontaneous epileptic seizures, reflecting similar neuropathological characteristics as seen in patients with temporal lobe epilepsy (TLE). Although this model has significantly contributed to increased knowledge of epileptogenesis, it is technically demanding, costly to operate and hence not suitable for high-throughput screening of anti-epileptic drugs (AEDs). Zebrafish, a vertebrate with complementary advantages to rodents, is an established animal model for epilepsy research. Here, we generated a novel KA-induced epilepsy model in zebrafish larvae that we functionally and pharmacologically validated. KA was administered by pericardial injection at an early zebrafish larval stage. The epileptic phenotype induced was examined by quantification of seizure-like behavior using automated video recording, and of epileptiform brain activity measured via local field potential (LFP) recordings. We also assessed GFP-labeled GABAergic and RFP-labeled glutamatergic neurons in double transgenic KA-injected zebrafish larvae, and examined the GABA and glutamate levels in the larval heads by liquid chromatography with tandem mass spectrometry detection (LC-MS/MS). Finally, KA-injected larvae were exposed to five commonly used AEDs by immersion for pharmacological characterization of the model. Shortly after injection, KA induced a massive damage and inflammation in the zebrafish brain and seizure-like locomotor behavior. An abnormal reorganization of brain circuits was observed, a decrease in both GABAergic and glutamatergic neuronal population and their associated neurotransmitters. Importantly, these changes were accompanied by spontaneous and continuous epileptiform brain discharges starting after a short latency period, as seen in KA rodent models and reminiscent of human pathology. Three out of five AEDs tested rescued LFP abnormalities but did not affect the seizure-like behavior. Taken together, for the first time we describe a chemically-induced larval zebrafish epilepsy model offering unique insights into studying epileptogenic processes in vivo and suitable for high-throughput AED screening purposes and rapid genetic investigations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".