Cathepsin protease activity modulates amyloid load in extracerebral amyloidosis
Bibliographic record
Abstract
In cerebral amyloidoses, such as Alzheimer's disease, proteolytic processing of the precursor protein is a fundamental mechanism of the disease, since it generates the amyloid protein. However, the putative significance of proteases in extracerebral amyloidoses is less well defined. In this study, we investigated the biological significance of cathepsin (Cath) B, CathK, and CathL in the pathology and pathogenesis of extracerebral amyloidoses by using the murine model of reactive or secondary AA amyloidosis with three different cathepsin-deficient mouse strains. Extracerebral AA amyloid was induced by injecting amyloid-enhancing factor and silver nitrate into CathB(-/-), CathK(-/-), and CathL(-/-) mice. Wild-type mice served as a control. CathK(-/-) mice deposited over 90% more amyloid and CathL(-/-) mice 60% less amyloid than the control (p < 0.0001). The amyloid load in CathB(-/-) mice did not differ from that in wild-type mice. In vitro degradation experiments with recombinant human and murine serum amyloid A (SAA) 1.1 and CathK and CathL showed that CathL generates a large number of differently sized SAA cleavage products. One of these fragments spans the heparin/heparan sulphate binding site and the neutral cholesterol ester hydrolase activating region of SAA. CathK showed only endoproteolytic activity and did not generate any AA amyloid-like peptides. This study provides unequivocal evidence that proteases modulate amyloid load in extracerebral amyloidosis. CathL was identified as an amyloid-promoting and CathK as an amyloid-retarding cysteine protease. CathB may only modulate the primary structure of the amyloid peptide without affecting amyloid load.
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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".