<scp>WONOEP</scp> appraisal: New genetic approaches to study epilepsy
Bibliographic record
Abstract
New genetic investigation techniques, including next-generation sequencing, epigenetic profiling, cell lineage mapping, targeted genetic manipulation of specific neuronal cell types, stem cell reprogramming, and optogenetic manipulations within epileptic networks are progressively unraveling the mysteries of epileptogenesis and ictogenesis. These techniques have opened new avenues to discover the molecular basis of epileptogenesis and to study the physiologic effects of mutations in epilepsy-associated genes on a multilayer level, from cells to circuits. This manuscript reviews recently published applications of these new genetic technologies in the study of epilepsy, as well as work presented by the authors at the genetic session of the XII Workshop on the Neurobiology of Epilepsy (WONOEP 2013) in Quebec, Canada. Next-generation sequencing is providing investigators with an unbiased means to assess the molecular causes of sporadic forms of epilepsy and has revealed the complexity and genetic heterogeneity of sporadic epilepsy disorders. To assess the functional impact of mutations in these newly identified genes on specific neuronal cell types during brain development, new modeling strategies in animals, including conditional genetics in mice and in utero knock-down approaches, are enabling functional validation with exquisite cell-type and temporal specificity. In addition, optogenetics, using cell-type-specific Cre recombinase driver lines, is enabling investigators to dissect networks involved in epilepsy. In addition, genetically encoded cell-type labeling is providing new means to assess the role of the nonneuronal components of epileptic networks such as glial cells. Furthermore, beyond its role in revealing coding variants involved in epileptogenesis, next-generation sequencing can be used to assess the epigenetic modifications that lead to sustained network hyperexcitability in epilepsy, including methylation changes in gene promoters and noncoding ribonucleic acid (RNA) involved in modifying gene expression following seizures. In addition, genetically based bioluminescent reporters are providing new opportunities to assess neuronal activity and neurotransmitter levels both in vitro and in vivo in the context of epilepsy. Finally, genetically rederived neurons generated from patient induced pluripotent stem cells and genetically modified zebrafish have become high-throughput means to investigate disease mechanisms and potential new therapies. Genetics has changed the field of epilepsy research considerably, and is paving the way for better diagnosis and therapies for patients with epilepsy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.169 | 0.057 |
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 source (direct Gemma or distilled Codex), 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".