Functional Genomics of Epilepsy and Associated Neurodevelopmental Disorders Using Simple Animal Models: From Genes, Molecules to Brain Networks
Bibliographic record
Abstract
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 twenty 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 even though they are central to understand the pathogenic mechanisms and to identify new therapeutic avenues. One main road block 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 network dynamic and brain activity. As a result, they represent models of choice to model aspects of the disease phenotype and unravel the pathogenic substratum in vivo.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".