De novo pathogenic variants in neuronal differentiation factor 2 (NEUROD2) cause a form of early infantile epileptic encephalopathy
Bibliographic record
Abstract
Background Early infantile epileptic encephalopathies are severe disorders consisting of early-onset refractory seizures accompanied often by significant developmental delay. The increasing availability of next-generation sequencing has facilitated the recognition of single gene mutations as an underlying aetiology of some forms of early infantile epileptic encephalopathies. Objectives This study was designed to identify candidate genes as a potential cause of early infantile epileptic encephalopathy, and then to provide genetic and functional evidence supporting patient variants as causative. Methods We used whole exome sequencing to identify candidate genes. To model the disease and assess the functional effects of patient variants on candidate protein function, we used in vivo CRISPR/Cas9-mediated genome editing and protein overexpression in frog tadpoles. Results We identified novel de novo variants in neuronal differentiation factor 2 (NEUROD2) in two unrelated children with early infantile epileptic encephalopathy. Depleting neurod2 with CRISPR/Cas9-mediated genome editing induced spontaneous seizures in tadpoles, mimicking the patients’ condition. Overexpression of wild-type NEUROD2 induced ectopic neurons in tadpoles; however, patient variants were markedly less effective, suggesting that both variants are dysfunctional and likely pathogenic. Conclusion This study provides clinical and functional support for NEUROD2 variants as a cause of early infantile epileptic encephalopathy, the first evidence of human disease caused by NEUROD2 variants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".