Targeting neuronal lysosomal dysfunction caused by β-glucocerebrosidase deficiency with an enzyme-based brain shuttle construct
Bibliographic record
Abstract
Mutations in glucocerebrosidase cause the lysosomal storage disorder Gaucher's disease and are the most common risk factor for Parkinson's disease. Therapies to restore the enzyme's function in the brain hold great promise for treating the neurological implications. Thus, we developed blood-brain barrier penetrant therapeutic molecules by fusing transferrin receptor-binding moieties to β-glucocerebrosidase (referred to as GCase-BS). We demonstrate that these fusion proteins show significantly increased uptake and lysosomal efficiency compared to the enzyme alone. In a cellular disease model, GCase-BS rapidly rescues the lysosomal proteome and lipid accumulations beyond known substrates. In a mouse disease model, intravenous injection of GCase-BS leads to a sustained reduction of glucosylsphingosine and can lower neurofilament-light chain plasma levels. Collectively, these findings demonstrate the potential of GCase-BS for treating GBA1-associated lysosomal dysfunction, provide insight into candidate biomarkers, and may ultimately open a promising treatment paradigm for lysosomal storage diseases extending beyond the central nervous system.
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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) | 0.000 | 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 | 0.001 |
| 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".