Early onset motor defects and electrographic seizures in a mouse model of the most common mutation in EEF1A2 related neurodevelopmental disorder, E122K
Bibliographic record
Abstract
Abstract De novo heterozygous missense mutations in EEF1A2 , encoding neuromuscular translation-elongation factor eEF1A2, are associated with developmental and epileptic encephalopathies. We used CRISPR/ Cas9 to recapitulate the most common mutation, E122K, in mice. Although E122K/+ mice were not observed to have convulsive seizures, they exhibit frequent electrographic seizures and EEG abnormalities, transient early motor delays and growth defects. Both E122K homozygotes and Eef1a2 -null mice develop progressive motor abnormalities phenotypes, with homozygotes reaching humane endpoints by P31. Surprisingly, E122K homozygotes did not exhibit the progressive spinal neurodegeneration which drives the null phenotype. The E122K protein is relatively stable in neurons yet highly unstable in skeletal myocytes, suggesting that the E122K/E122K phenotype is instead driven by loss-of-function in muscle. Importantly, E122K homozygotes developed abnormalities far earlier than nulls, suggesting a toxic gain-of-function. This novel mouse model represents the first animal model of an EEF1A2 missense mutation with face-valid phenotypes and has provided mechanistic insights needed to inform rational treatment design.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".