Molecular pathogenesis of megalencephalic leukoencephalopathy with subcortical cysts: mutations in MLC1 cause folding defects
Bibliographic record
Abstract
Megalencephalic leukoencephalopathy with subcortical cysts (MLC) is a rare type of leukodystrophy, most often caused by mutations in the MLC1 gene. MLC1 is an oligomeric plasma membrane (PM) protein of unknown function expressed mainly in glial cells and neurons. Most disease-causing missense mutations dramatically reduced the total and PM MLC1 expression levels in Xenopus oocytes and mammalian cells. The impaired expression of the mutants was verified in primary cultures of rat astrocytes, as well as human monocytes, cell types that endogenously express MLC1, demonstrating the relevance of the tissue culture models. Using a combination of biochemical, pharmacological and imaging methods, we also demonstrated that increased endoplasmatic reticulum-associated degradation and endo-lysosomal-associated degradation can contribute to the cell surface expression defect of the mutants. Based on these results, we suggest that MLC1 mutations reduce protein levels in vivo. Since the expression defect of the mutants could be rescued by exposing the mutant-protein expressing cells to low temperature and glycerol, a chemical chaperone, we propose that MLC belongs to the class of conformational diseases. Therefore, we suggest the use of pharmacological strategies that improve MLC1 expression to treat MLC patients.
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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 | 0.000 | 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) | 0.001 | 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".