Severity of vanishing white matter disease does not correlate with deficits in eIF2B activity or the integrity of eIF2B complexes
Bibliographic record
Abstract
Autosomal recessive mutations in eukaryotic initiation factor 2B (eIF2B) cause leukoencephalopathy vanishing white matter with a wide clinical spectrum. eIF2B comprises five subunits (α-ε; genes EIF2B1, 2, 3, 4 and 5) and is the guanine nucleotide-exchange factor (GEF) for eIF2. It plays a key role in protein synthesis. Here, we have studied the functional effects of selected VWM mutations in EIF2B2-5 by coexpressing mutated and wild-type subunits in human cells. The observed functional effects are very diverse, including defects in eIF2B complex integrity; binding to the regulatory α-subunit; substrate binding; and GEF activity. Activity data for recombinant eIF2B complexes agree closely with those for patient-derived cells with the same mutations. Some mutations do not affect these parameters even though they cause severe disease. These findings are important for three reasons; they demonstrate that measuring eIF2B activity in patients' cells has limited value as a diagnostic test; they imply that severe disease can result from alterations in eIF2B function other than defects in complex integrity, substrate binding or GEF activity, and last, the diversity of functional effects of VWM mutations implies that seeking agents to manage or treat VWM should focus on downstream effectors of eIF2B, not restoring eIF2B activity.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".