Cathepsin D: Analysis of its potential role as an amyloid beta degrading protease
Bibliographic record
Abstract
Proteolysis catalyzed by the major lysosomal aspartyl protease cathepsin-D (CTSD) appears to be of pivotal importance for proteostasis within the central nervous system and in neurodegeneration. Neuronal Ceroid Lipofuscinosis (NCL) type 10 is caused by a lack of CTSD leading to a defective autophagic flow and pathological accumulation of proteins. We previously demonstrated a therapeutic-relevant clearance of protein aggregates after dosing a NCL10 mouse model with recombinant human pro-cathepsin-D (proCTSD). Similar results could be achieved in cells and mice accumulating α-synuclein. Prompted by these positive effects and our in vitro findings showing that cathepsin-D can cleave the Alzheimer's Disease (AD)-causing amyloid beta peptides (Aβ), we envisaged that such a treatment with proCTSD could similarly be effective in clearance of potentially toxic Aβ species. We demonstrated that CTSD is able to cleave human Aβ1–42 by using liquid chromatography-mass spectrometry. Intracerebral dosing of proCTSD in a NCL10 (CTSD knockout) mouse model revealed uptake and processing of CTSD to its mature and active form. However, the re-addition of CTSD did not obviously affect intracellular APP processing or the generation of soluble APP and Aβ-species. ProCTSD treated HEK cells in comparison with untreated cells were found to contain comparable levels of soluble and membrane bound APP and Aβ-species. Also, the early intracranial application (P1 and P20) of proCTSD in the 5xFAD mouse model did not change Aβ pathology, plaque number and plaque composition and neuroinflammation, however we observed an increased level of Aβ1–42 in the CSF. Our data confirm proteolytic cleavage of human Aβ1–42 by CTSD but exclude a prominent role of CTSD in APP processing and Aβ degradation in our in vitro and in vivo models.
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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 | 0.001 | 0.001 |
| 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".